{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Classification of Cancer or Non-Cancer with Classifiers\n",
    "\n",
    "## Summary\n",
    "\n",
    "* Load and process dataset by subsetting the largest nodule for each sample\n",
    "* Label as cancer or non-cancer. Alternatively, create random labels with same ratio of classes\n",
    "* For multinomial naive bayes classification, transform numerical data into categorical data by discretizing the features by rounding or transforming to percentiles\n",
    "* Set up stratified K-fold and perform cross validation with GaussianNB, MultinomialNB, Logisitic Regression, Random Forest, Gradient Boosting, SVM w/rbf kernel, SVM w/linear kernel\n",
    "* Create ensemble of classifiers\n",
    "* Compare performance with classifiers trained with random labels\n",
    "* Optimize model parameters with grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\sklearn\\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#EDIT HERE##############################\n",
    "\n",
    "#input list of tables generated from TrainUnet.ipynb\n",
    "tablelist=['DSBPatientNoduleIndex.csv','DSBPatientNoduleIndex369-629.csv','DSBPatientNoduleIndex630-1199.csv','DSBPatientNoduleIndex1200-1594.csv', 'DSBPatientNoduleIndexTest.csv']\n",
    "\n",
    "########################################\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import dicom\n",
    "import os\n",
    "import scipy.ndimage\n",
    "import time\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "import h5py\n",
    "from sklearn.cluster import KMeans\n",
    "from skimage import measure, morphology\n",
    "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n",
    "\n",
    "\n",
    "import random\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, ExtraTreesClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, classification_report,log_loss\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from scipy.ndimage.measurements import center_of_mass, label\n",
    "from skimage.measure import regionprops\n",
    "from sklearn.cross_validation import ShuffleSplit\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n",
    "from scipy.stats import percentileofscore\n",
    "\n",
    "import keras\n",
    "from keras.utils import to_categorical\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
    "from keras import backend as K\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import plot_model\n",
    "#import unet_model\n",
    "import scipy as sp\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and process dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Area</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>DiameterMajor</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>LargestNoduleArea</th>\n",
       "      <th>MeanHU</th>\n",
       "      <th>NoduleIndex</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Spiculation</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0.1.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>89.359329</td>\n",
       "      <td>10.645108</td>\n",
       "      <td>13.594830</td>\n",
       "      <td>0.713720</td>\n",
       "      <td>89</td>\n",
       "      <td>-272.820221</td>\n",
       "      <td>55</td>\n",
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       "      <td>71.400085</td>\n",
       "      <td>9.507892</td>\n",
       "      <td>12.956937</td>\n",
       "      <td>0.801334</td>\n",
       "      <td>71</td>\n",
       "      <td>-236.535217</td>\n",
       "      <td>56</td>\n",
       "      <td>0015ceb851d7251b8f399e39779d1e7d</td>\n",
       "      <td>0.910256</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>39.051132</td>\n",
       "      <td>6.863663</td>\n",
       "      <td>8.486004</td>\n",
       "      <td>0.708520</td>\n",
       "      <td>37</td>\n",
       "      <td>-173.769226</td>\n",
       "      <td>57</td>\n",
       "      <td>0015ceb851d7251b8f399e39779d1e7d</td>\n",
       "      <td>0.948718</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>63.034634</td>\n",
       "      <td>8.956232</td>\n",
       "      <td>12.397636</td>\n",
       "      <td>0.784063</td>\n",
       "      <td>63</td>\n",
       "      <td>-307.714294</td>\n",
       "      <td>58</td>\n",
       "      <td>0015ceb851d7251b8f399e39779d1e7d</td>\n",
       "      <td>0.797468</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>25.141571</td>\n",
       "      <td>5.753627</td>\n",
       "      <td>7.122801</td>\n",
       "      <td>0.662585</td>\n",
       "      <td>26</td>\n",
       "      <td>-366.500000</td>\n",
       "      <td>59</td>\n",
       "      <td>0015ceb851d7251b8f399e39779d1e7d</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>42.820370</td>\n",
       "      <td>7.312733</td>\n",
       "      <td>9.713572</td>\n",
       "      <td>0.820839</td>\n",
       "      <td>42</td>\n",
       "      <td>-319.666656</td>\n",
       "      <td>66</td>\n",
       "      <td>0015ceb851d7251b8f399e39779d1e7d</td>\n",
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       "      <td>6</td>\n",
       "      <td>55.689487</td>\n",
       "      <td>8.519076</td>\n",
       "      <td>9.886418</td>\n",
       "      <td>0.610325</td>\n",
       "      <td>57</td>\n",
       "      <td>-367.298248</td>\n",
       "      <td>67</td>\n",
       "      <td>0015ceb851d7251b8f399e39779d1e7d</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>5.780643</td>\n",
       "      <td>2.763953</td>\n",
       "      <td>3.265986</td>\n",
       "      <td>0.790569</td>\n",
       "      <td>6</td>\n",
       "      <td>-446.000000</td>\n",
       "      <td>110</td>\n",
       "      <td>0015ceb851d7251b8f399e39779d1e7d</td>\n",
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       "      <td>4.513517</td>\n",
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       "      <td>16</td>\n",
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       "      <td>116</td>\n",
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       "      <td>0.974359</td>\n",
       "      <td>9</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   index       Area   Diameter  DiameterMajor  Eccentricity  \\\n",
       "0      0  89.359329  10.645108      13.594830      0.713720   \n",
       "1      1  71.400085   9.507892      12.956937      0.801334   \n",
       "2      2  39.051132   6.863663       8.486004      0.708520   \n",
       "3      3  63.034634   8.956232      12.397636      0.784063   \n",
       "4      4  25.141571   5.753627       7.122801      0.662585   \n",
       "5      5  42.820370   7.312733       9.713572      0.820839   \n",
       "6      6  55.689487   8.519076       9.886418      0.610325   \n",
       "7      7   5.780643   2.763953       3.265986      0.790569   \n",
       "8      8  16.465515   4.513517       6.571094      0.845376   \n",
       "9      9  37.294971   6.955796       8.292937      0.707234   \n",
       "\n",
       "   LargestNoduleArea      MeanHU  NoduleIndex  \\\n",
       "0                 89 -272.820221           55   \n",
       "1                 71 -236.535217           56   \n",
       "2                 37 -173.769226           57   \n",
       "3                 63 -307.714294           58   \n",
       "4                 26 -366.500000           59   \n",
       "5                 42 -319.666656           66   \n",
       "6                 57 -367.298248           67   \n",
       "7                  6 -446.000000          110   \n",
       "8                 16 -468.562500          116   \n",
       "9                 38 -382.947357          119   \n",
       "\n",
       "                            Patient  Spiculation  Unnamed: 0  Unnamed: 0.1  \\\n",
       "0  0015ceb851d7251b8f399e39779d1e7d     0.864078           0             0   \n",
       "1  0015ceb851d7251b8f399e39779d1e7d     0.910256           1             1   \n",
       "2  0015ceb851d7251b8f399e39779d1e7d     0.948718           2             2   \n",
       "3  0015ceb851d7251b8f399e39779d1e7d     0.797468           3             3   \n",
       "4  0015ceb851d7251b8f399e39779d1e7d     0.866667           4             4   \n",
       "5  0015ceb851d7251b8f399e39779d1e7d     0.954545           5             5   \n",
       "6  0015ceb851d7251b8f399e39779d1e7d     0.863636           6             6   \n",
       "7  0015ceb851d7251b8f399e39779d1e7d     1.000000           7             7   \n",
       "8  0015ceb851d7251b8f399e39779d1e7d     0.800000           8             8   \n",
       "9  0015ceb851d7251b8f399e39779d1e7d     0.974359           9             9   \n",
       "\n",
       "   Unnamed: 0.1.1  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "5             NaN  \n",
       "6             NaN  \n",
       "7             NaN  \n",
       "8             NaN  \n",
       "9             NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load tables and concatenate\n",
    "table=pd.read_csv(tablelist[0])\n",
    "if len(tablelist)>1:\n",
    "    for file in tablelist[1:]:\n",
    "        temptable=pd.read_csv(file)\n",
    "        table=pd.concat([table,temptable])\n",
    "table=table.reset_index()\n",
    "\n",
    "table[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n"
     ]
    }
   ],
   "source": [
    "#Subset largest nodule for each sample and label as cancer or non-cancer\n",
    "malignantlabel=[]\n",
    "malignancytable=pd.concat([pd.read_csv(\"stage1_labels.csv\"),pd.read_csv(\"stage1_solution.csv\")])\n",
    "patients=malignancytable[\"id\"].values\n",
    "index=0\n",
    "indx=[]\n",
    "for patient in patients:\n",
    "    nodulearea=table[[\"LargestNoduleArea\",\"NoduleIndex\",\"MeanHU\"]].loc[table[\"Patient\"]==patient]\n",
    "    if len(nodulearea)>0:\n",
    "        malignantlabel.append(malignancytable[\"cancer\"].loc[malignancytable[\"id\"]==patient].values[0].astype(np.bool))\n",
    "        indx.append(nodulearea[\"NoduleIndex\"].loc[nodulearea[\"LargestNoduleArea\"]==max(nodulearea[\"LargestNoduleArea\"])].index[0])\n",
    "inputfeatures=table.iloc[indx]\n",
    "inputfeatures[\"label\"]=malignantlabel\n",
    "TFratio=len([a for a in malignantlabel if a==True])/len(malignantlabel)\n",
    "TFratio\n",
    "randomlabel=np.random.choice([0, 1], size=(len(malignantlabel),), p=[(1-TFratio), TFratio])\n",
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(inputfeatures,malignantlabel,test_size=.30, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ixNEPt61ERHqoSHL5y04H0Vd020pEpKGGycXdv9aNQEREZP5o2OdiZseb2bfM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\nNeC+kXjvCUL5/8ylE0SKUnI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x100f4ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111a5278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DQihkjF8EU2F1TLTIU6FQvD0Yyy328Hmvbwoh/l4I8e6ZFHC242v38eXdX0ZIQVleGbFU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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119a07b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6cminSNodOCySVSMbnRoRQ0lW/rwSICLOioglna2vFT8mma1r1iWcCKxHSBf4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\nM7OccyIwM8u5/wVSSgoN//ExLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1199b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LOCcCM7OMcyIwM8u4/w+Vbyli0j4MbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119cd978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117545c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SD+ABYGJErN9+DYCICEmtXkfSOGAcwIABAwoMs/p5VIyZlVuhTwT/LukaYO9kruIZwKMd\nnSSpllwSuDcits9itlpSv2R/P2BNa+dGxJSIaIyIxvr6+gLDNDOzYhWaCK4C1gKvAeOBx8nNX9wm\n5f7XfyqwJCJ+mLfrEWBssjwWeLiYgM3MrLQKLTq3Dfhp8lOoE4AxwGuSFiTbrgH+BbhP0leBt4Hz\nirimmZmVWKG1ht6ilT6BiDi4rXMi4mlyJatbc3JB0ZmZWeqKqTW0XR1wLtC79OFUt65QH7/YzuZK\ndU4X01buQDdLV6FfKFuX97MyIm4hN6G9mZlVuUJfDR2Vt9qN3BNCMXMZmJlZF1XoH/Ob8pa3kNQI\nKnk0ZmZWdoWOGvrrtAMxM7PKKPTV0JXt7d/pewJmZlZFihk1NILcl8EA/hZ4EViWRlBWnbrCqCkz\nK16hiaA/cFRSRRRJzcCvIuLv0grMzMzKo9ASE32Bj/PWP8ZVQ83M9giFPhHcBbwoaWayfg6fzClg\nZmZVrNBRQ/9D0hPAScmmiyLiN+mFZWZm5VLoqyGATwHrI+JHwApJA1OKyczMyqjQ4aPXkRs5NAj4\nGVAL3EOuwqh1YPtoGo+kaV1bo41cY8isPAp9IhgNnAV8ABARfwR6phWUmZmVT6GJ4OOICJJS1JL2\nSS8kMzMrp0ITwX2Sbgd6SboYeJLiJqkxM7MuqtBRQzcmcxWvJ9dP8J2ImJ1qZGZmVhYdJgJJNcCT\nSeE5//G3VJWjg9ilMMx21OGroYjYCmyT9JkyxGNmZmVW6DeLN5KbhH42ycghgIi4vK0TJE0DzgTW\nRMQRybZm4GJgbXLYNRHxeCfiNjOzEik0ETyY/BTjTuA2cuUp8t0cETcWeS0zM0tJu4lA0oCIeCci\niq4rFBHzJDV0NjAzMyuPjvoIHtq+IOmBEt3zMkmvSpomad8SXdPMzDqpo0SgvOWDS3C/HyfXGQas\nYse5kHe8sTRO0suSXl67dm1bh5mZ2W7qKBFEG8udEhGrI2JrRGwj94W0Y9o5dkpENEZEY319/e7e\n2szM2tBRZ/FQSevJPRnsnSyTrEdEfLqYm0nqFxGrktXRwKKiojUzs5JrNxFERE1nLyzpF8BIoI+k\nFcB1wEhJw8g9XSwHxnf2+mZmVhqFDh8tWkSc38rmqWndz8zMOie1RGC7cn399HQ050NHbV+KshNd\noXRFV4jBqk8xM5SZmdkeyInAzCzjnAjMzDLOicDMLOOcCMzMMs6jhnZTlkYCVdtnLSTeavtMZmnw\nE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGedRQEaqyjsvcuZ8sjxxZqSjKxqOAzIrnJwIzs4xz\nIjAzyzgnAjOzjHMiMDPLOHcWp2SP67QsstN5j/v8u6GjSXPMKs1PBGZmGZdaIpA0TdIaSYvytvWW\nNFvSsuT3vmnd38zMCpPmE8GdwOk7bbsKeCoiDgGeStbNzKyCUksEETEPeG+nzWcD05Pl6cA5ad3f\nzMwKU+4+gr4RsSpZfhfoW+b7m5nZTio2aigiQlK0tV/SOGAcwIABA8oW184yMfqlVGUo8q+zu9eq\noEJKiexpI4GqsnyKlUy5nwhWS+oHkPxe09aBETElIhojorG+vr5sAZqZZU25E8EjwNhkeSzwcJnv\nb2ZmO0lz+OgvgOeAQZJWSPoq8C/AqZKWAack62ZmVkGp9RFExPlt7Do5rXuamVnxXGKiGpRzToG0\n7lXIdds6Jo2YSnTNYgYTtHWsO2et0lxiwsws45wIzMwyzonAzCzjnAjMzDLOicDMLOM8aqialXM0\nUVv37Yoq1S552h1NVGB8LdfIO765eW5rh5rtFj8RmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZxH\nDe0pdqeWj7WptZE7ZWm75mZgbiubR+Yt77rfrDP8RGBmlnFOBGZmGedEYGaWcU4EZmYZ587iRH5J\ngExMFFKOMhG7c49ylrFIe+KbDpTjv7227lHMxDrFXLftE5pbX7aK8hOBmVnGVeSJQNJyYAOwFdgS\nEY2ViMPMzCr7auivI+JPFby/mZnhV0NmZplXqUQQwJOS5ksaV6EYzMyMyr0aOjEiVkraH5gtaWlE\nzMs/IEkQ4wAGDBhQ0pt3NNJhd0dSdMilHkoj7dE+hVyzkpP05I26yas80fFpaf/3DR2ODmpmLiRx\nZGKUXhdXkSeCiFiZ/F4DzASOaeWYKRHRGBGN9fX15Q7RzCwzyp4IJO0jqef2ZWAUsKjccZiZWU4l\nXg31BWZK2n7/n0fE/65AHGZmRgUSQUT8Hhha7vuamVnrXGKiFNrqYCx2joBi77U7x3QVpfr8abTL\n7tyrM+bObelA7XLyO6ZbmScBgJG0ekzzzseVistVlIy/R2BmlnFOBGZmGedEYGaWcU4EZmYZ50Rg\nZpZxmRk1VOqv1bd5vWoasVMplZp0pgq0OSKnI618zua5I4u/Tv7otubmnUb/dOJ6QHNS/6Kz56ep\n2WUuAD8RmJllnhOBmVnGORGYmWWcE4GZWcY5EZiZZVxmRg21peDRRFU2+qQksviZy2032rjTI4za\n0069o47uV/TIvO2fvYP7NTe3ft826xkVUneorWPaql/UxiRARY826qL1kfxEYGaWcU4EZmYZ50Rg\nZpZxTgRmZhm353cWt3TIzC1s0pg0Jx0x25N18N94IZ3brR3TnN8729Z5rRxTcEmL3ZkQKI3O352v\nU4ZOZT8RmJllXEUSgaTTJf1W0puSrqpEDGZmllP2RCCpBvhX4D8BhwPnSzq83HGYmVlOJZ4IjgHe\njIjfR8THwC+BsysQh5mZUZlEcADwh7z1Fck2MzOrAEVEeW8o/Rfg9Ij4b8n6GODYiLh0p+PGAeOS\n1UHAb8saaHXoA/yp0kF0YW6ftrlt2rentM9BEVHf0UGVGD66Ejgwb71/sm0HETEFmFKuoKqRpJcj\norHScXRVbp+2uW3al7X2qcSroZeAQyQNlPRXwH8FHqlAHGZmRgWeCCJii6RLgf8D1ADTImJxueMw\nM7OcinyzOCIeBx6vxL33MH511j63T9vcNu3LVPuUvbPYzMy6FpeYMDPLOCeCKiFpmqQ1khblbest\nabakZcnvfSsZY6VIOlDSHEmvS1os6Ypku9sHkFQn6UVJC5P2+W6y3e2TkFQj6TeSHkvWM9U2TgTV\n407g9J22XQU8FRGHAE8l61m0BZgUEYcDxwETkrIlbp+czUBTRAwFhgGnSzoOt0++K4AleeuZahsn\ngioREfOA93bafDYwPVmeDpxT1qC6iIhYFRGvJMsbyP2DPgC3DwCRszFZrU1+ArcPAJL6A2cAd+Rt\nzlTbOBFUt74RsSpZfhfoW8lgugJJDcBw4AXcPi2SVx8LgDXA7Ihw+3ziFuBbwLa8bZlqGyeCPUTk\nhn9legiYpB7AA8DEiFifvy/r7RMRWyNiGLlv8h8j6Yid9meyfSSdCayJiPltHZOFtnEiqG6rJfUD\nSH6vqXA8FSOpllwSuDciHkw2u312EhF/BuaQ629y+8AJwFmSlpOrhNwk6R4y1jZOBNXtEWBssjwW\neLiCsVSMJAFTgSUR8cO8XW4fQFK9pF7J8t7AqcBS3D5ExNUR0T8iGsiVu/l1RPwdGWsbf6GsSkj6\nBTCSXFXE1cB1wEPAfcAA4G3gvIjYuUN5jyfpROD/Aq/xyXvea8j1E7h9pCHkOjxryP3P330R8U+S\n9sPt00LSSOC/R8SZWWsbJwIzs4zzqyEzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyKwTJC0VdKC\npPrmQkmTJHVL9jVKmpzy/c9JCuGZdTkePmqZIGljRPRIlvcHfg48ExHXlen+dwKPRcT9RZyzV0Rs\nSS8qsxwnAsuE/ESQrB8MvETuC3pf5JMvEh0D/AioAz4ELoqI30r6e3IVKPcBDgFuBP4KGEOuzPPf\nRMR7kj4L/CtQD2wCLgZ6A48B7yc/X0rC2OG4iFiaJIyPyBXOeyYirkynRcw+UZE5i80qLSJ+L6kG\n2H+nXUuBkyJii6RTgO/zyR/uI8j9ga4D3gT+ISKGS7oZuJBcFcspwNciYpmkY4F/i4gmSY+Q90Qg\n6amdjwOakvv0B46PiK0pfXyzHTgRmO3oM8B0SYeQqzhZm7dvTjLfwQZJ7wOPJttfA4Yk1U+PB2bk\nyh8B0H3nGxRw3AwnASsnJwLLpOTV0FZyVSUPy9v1z+T+4I9O5jaYm7dvc97ytrz1beT+LXUD/pyU\ne25PR8d9UMBHMCsZjxqyzJFUD/wEuC127ST7DLAyWf77Yq6bzIHwlqRzk/tI0tBk9wagZwHHmZWd\nE4Flxd7bh48CTwKzgO+2ctz1wP+U9Bs698R8AfBVSQuBxeSmPIRcrftvJhOkf7ad48zKzqOGzMwy\nzk8EZmYZ50RgZpZxTgRmZhnnRGBmlnFOBGZmGedEYGaWcU4EZmYZ50RgZpZx/x+fZ8JTneQpcAAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11931080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c97be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xp/dwt6QdthaLdUO1a277p+d/gA3pdw1wA3Bkmt8e2DlNDyG7I1FAAyU1z0vn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\nzKzgnAjMzAru/wOxWMTLj1C2fQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117439e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ek4GziGZrHObun5nZGqCqSDH+T2Ceu59iZjVE/ztu8GHGsgG/dvfbM3cOXSWj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\niIiknBKBiEjK/X9BA3e3vhgisQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ab83c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exploratory analysis through data visualization\n",
    "plt.scatter(table[\"LargestNoduleArea\"],table[\"MeanHU\"],color=\"red\")\n",
    "plt.xlabel(\"Area (Pixels)\")\n",
    "plt.ylabel(\"Mean radiodensity (HU)\")\n",
    "plt.show()\n",
    "\n",
    "plt.hist(table[\"LargestNoduleArea\"],bins=100,alpha=0.5, color=\"red\")\n",
    "plt.xlabel(\"Area (Pixels)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()\n",
    "\n",
    "plt.hist(table[\"MeanHU\"],bins=100,alpha=0.5, color=\"red\")\n",
    "plt.xlabel(\"Mean radiodensity (HU)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(inputfeatures[\"LargestNoduleArea\"].loc[inputfeatures[\"label\"]==True],inputfeatures[\"MeanHU\"].loc[inputfeatures[\"label\"]==True],alpha=0.5, color=\"red\")\n",
    "plt.scatter(inputfeatures[\"LargestNoduleArea\"].loc[inputfeatures[\"label\"]==False],inputfeatures[\"MeanHU\"].loc[inputfeatures[\"label\"]==False], alpha=0.5, color=\"green\")\n",
    "plt.xlabel(\"Area (Pixels)\")\n",
    "plt.ylabel(\"Mean radiodensity (HU)\")\n",
    "plt.legend([\"Cancer\",\"No Cancer\"])\n",
    "plt.show()\n",
    "\n",
    "plt.hist(inputfeatures[\"LargestNoduleArea\"].loc[inputfeatures[\"label\"]==True],bins=100,alpha=0.5, color=\"red\")\n",
    "plt.hist(inputfeatures[\"LargestNoduleArea\"].loc[inputfeatures[\"label\"]==False],bins=100,alpha=0.5, color=\"green\")\n",
    "plt.xlabel(\"Area (Pixels)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend([\"Cancer\",\"No Cancer\"])\n",
    "plt.show()\n",
    "\n",
    "plt.hist(inputfeatures[\"MeanHU\"].loc[inputfeatures[\"label\"]==True],bins=100,alpha=0.5, color=\"red\")\n",
    "plt.hist(inputfeatures[\"MeanHU\"].loc[inputfeatures[\"label\"]==False],bins=100,alpha=0.5, color=\"green\")\n",
    "plt.xlabel(\"Mean radiodensity (HU)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend([\"Cancer\",\"No Cancer\"])\n",
    "plt.show()\n",
    "\n",
    "plt.hist(inputfeatures[\"NoduleIndex\"].loc[inputfeatures[\"label\"]==True],bins=100,alpha=0.5, color=\"red\")\n",
    "plt.hist(inputfeatures[\"NoduleIndex\"].loc[inputfeatures[\"label\"]==False],bins=100,alpha=0.5, color=\"green\")\n",
    "plt.xlabel(\"Slice #\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend([\"Cancer\",\"No Cancer\"])\n",
    "plt.show()\n",
    "\n",
    "plt.hist(inputfeatures[\"Diameter\"].loc[inputfeatures[\"label\"]==True],bins=100,alpha=0.5, color=\"red\")\n",
    "plt.hist(inputfeatures[\"Diameter\"].loc[inputfeatures[\"label\"]==False],bins=100,alpha=0.5, color=\"green\")\n",
    "plt.xlabel(\"Diameter\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend([\"Cancer\",\"No Cancer\"])\n",
    "plt.show()\n",
    "\n",
    "plt.hist(inputfeatures[\"DiameterMajor\"].loc[inputfeatures[\"label\"]==True],bins=100,alpha=0.5, color=\"red\")\n",
    "plt.hist(inputfeatures[\"DiameterMajor\"].loc[inputfeatures[\"label\"]==False],bins=100,alpha=0.5, color=\"green\")\n",
    "plt.xlabel(\"Diameter Major Axis\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend([\"Cancer\",\"No Cancer\"])\n",
    "plt.show()\n",
    "\n",
    "plt.hist(inputfeatures[\"Eccentricity\"].loc[inputfeatures[\"label\"]==True],bins=100,alpha=0.5, color=\"red\")\n",
    "plt.hist(inputfeatures[\"Eccentricity\"].loc[inputfeatures[\"label\"]==False],bins=100,alpha=0.5, color=\"green\")\n",
    "plt.xlabel(\"Ratio of major axis/minor axis length\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend([\"Cancer\",\"No Cancer\"])\n",
    "plt.show()\n",
    "\n",
    "plt.hist(inputfeatures[\"Spiculation\"].loc[inputfeatures[\"label\"]==True],bins=100,alpha=0.5, color=\"red\")\n",
    "plt.hist(inputfeatures[\"Spiculation\"].loc[inputfeatures[\"label\"]==False],bins=100,alpha=0.5, color=\"green\")\n",
    "plt.xlabel(\"Ratio of area/convex hull area\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend([\"Cancer\",\"No Cancer\"])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Normalize data\n",
    "featureslist=[\"MeanHU\",\"NoduleIndex\",\"Diameter\",\"DiameterMajor\",\"Spiculation\",\"Eccentricity\"]\n",
    "normalizedfeatures={}\n",
    "for feature in featureslist:\n",
    "    mean=np.mean(inputfeatures[feature])\n",
    "    std=np.std(inputfeatures[feature])\n",
    "    normalizedfeatures[feature]=(inputfeatures[feature].values-mean)/std\n",
    "normalizedfeatures=pd.DataFrame(normalizedfeatures)\n",
    "rawfeatures=inputfeatures.copy()\n",
    "inputfeatures=normalizedfeatures.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1512"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(inputfeatures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "featureslist=[\"MeanHU\",\"NoduleIndex\",\"Diameter\",\"DiameterMajor\",\"Spiculation\",\"Eccentricity\"]\n",
    "\n",
    "#diameterpercentiles=[]\n",
    "percentilefeatures={}\n",
    "\n",
    "for i,feature in enumerate(featureslist):\n",
    "    featurestemp=[]\n",
    "    for j,val in enumerate(rawfeatures[feature]):\n",
    "        featurestemp.append(percentileofscore(rawfeatures[feature],val))\n",
    "        percentilefeatures[feature]=np.round(np.array(featurestemp)/2,0)\n",
    "percentilefeatures=pd.DataFrame(percentilefeatures)\n",
    "\n",
    "roundedfeatures={}\n",
    "roundedfeatures[featureslist[0]]=np.round(rawfeatures[featureslist[0]]/100,0)*100\n",
    "\n",
    "roundedfeatures[featureslist[1]]=np.round(rawfeatures[featureslist[1]]/20,0)*20\n",
    "roundedfeatures[featureslist[2]]=np.round(rawfeatures[featureslist[2]]/3,0)*3\n",
    "roundedfeatures[featureslist[3]]=np.round(rawfeatures[featureslist[3]]/4,0)*4\n",
    "roundedfeatures[featureslist[4]]=np.round(rawfeatures[featureslist[4]]/2.5,2)*2.5\n",
    "roundedfeatures[featureslist[5]]=np.round(rawfeatures[featureslist[5]]/4,2)*4\n",
    "roundedfeatures=pd.DataFrame(roundedfeatures)\n",
    "\n",
    "XtrainR, XtestR, YtrainR, YtestR = train_test_split(roundedfeatures,malignantlabel,test_size=.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O3lMwM7OMi4KZmWVcFMzMLONzCmXiY+5m1h55T8HMzDIuCmZmlnFRMDOzTNFFQVJXSduX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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d9860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gqkgH0VNyI9Ox5LyrsJmxLVVHpfGd+eywW6lFQBfHl3U5MFXSjXz2Xm9Dcujy\nzznaLiK+F8mNTdWKr/b2d5T3r9JtL6tNn1OAFr15a0uSY/K5bl6qJxbgdeBukuP5jd74VEn7LRR/\nFskdkPX9cd0cEb+oQnztB6ta8U3pf3/gQNa8yOGFcnEtGL8D9V9k0Vrx1d7+Dvv+VbrtZetu60mh\nUunVSz2BKRGxvKR8v4jIM1RGaV3XRMRRTVh/N+DFiFgmaT2SL/hdgFnAf0fEskbiu5KcaF8UEQ9J\nOhLYA3gBmBARH+fogz+YBX24WpukLSJicQXx3SNiSUv2qS3x+5eq9PhTW56A04GXgDuB+cBBJcue\nbiR2Yj3T8trXOdufRXpsEJgA/A7YC/gJcHuO+OuAm9I2rwFuB44CriQ5pFT197gZ/ydbVBjfvZX6\nuTFwPsl5raXAEpJzFOcDm+SI3wj4Rfr/dnidZRfniN+snmk+sCmwWY7484Ee6evBwDySY9WvAHvn\niB8CPAJcS7J39SDwDjAV+LLfv+K2v9Jtb7RvLf1haUsT8BywQfq6DzANOCOdf6aR2KfTD8RwYO/0\n39fS143+UaR1zC6tr86yGTniZ6b/diY5bFSTzqt2WSPxHf2D2ewvNiq/8uu2tP+jSZL6bcA69f0t\nNBD/KfBynenj9N95ef72S14/Auyavv4SMC1H/FPAKJI91QXAIWn5PsCTOeI7+vtXyZWPFW17o32r\ntIK2PAGz6sxvQHJJ4m9p5EuZZIiQ/0i/SAalZY3+MdWp4xbguPT1FcCQkj+sqTninye54mBT4D3S\nL1KgG/muvunoH8xmf7FR+ZVfM+rMnw08QTL6ZZ737sz0b3WnkrKXm/C3N5vP9lKnNPS+lol/puT1\nPxta5vev5be/0m1vtG+VVtCWJ+CvpF/oJWWdgauBT3LW0ZPky/0PdT8cOWI3JjnU8w/g/9IvxHnA\nY8DAHPH/ka7/CsmhsIeBS0n2gH6SI76jfzCb/cUGPAB8H9iypGxLkoT6UM6+d6pTdizJIcVXmvi3\n91tgQ5rwowQ4Ld2GrwLnABeS7OX+FLgmR/yTJPd5fDP9+xudlu9NvoTc0d+/Zm9/S2x72forraAt\nT+kfxecaWLZnE+v6OsnJ4eb0YyNgIMkhkC2bGLsVsFX6ehPgEGBoztiO/sFs9hcbyd7ZBSSH3t4m\nOfw2Oy3Lc+jrl8CIesr3I+eT+0piDgSmAK83MW44yTmpZ0h+SEwCxlLPDaH1xA4k2dO8D9g+fe/f\nSf/v98gxrFqnAAAEZ0lEQVQR39Hfv7rb/3a6/b9sbPtbctvrrb/SCjy13anOH+bSOh/MTXPEr80f\nzDVu7qknttIvtu2BEaTnpUq3P2fftyc5VFU3flRT44F1gR1bqP288TtUGD+Uzw75DSDZc9y/Cf/3\npfH9gf+sIH4n4IcVxDe5//XU1+gPmTKxVzc3tu7U4S9JtfpJOi4irmjNeEnrAttGxPPVaL8p8ZJO\nB04hSaKDSC5QuCtd9nRElB16W9JpJDdYNje+0vZbIv5kkh8UzYn/Ccn5nM4k5+WGkjw9bF/g/og4\nr4nxu5GcV2pufKXtNzV+Yj3FXyU5pE1EHNiEWAFfyRObS0tlF0/ta6KJ50c6WjwVXLnm+Cy+BlgP\neBfYKC1fl3xXzrX1+GZfvUiyV1zRlY/lprY+zIVVQNLMhhbx2V3ajq9fp0hvdoyI+ZKGA7dK6p3G\nN6ajx6+KZNSBlZL+ERHvpnW9L+nTDhA/hOQ56mcD34uIGZLej4jHcsQOriC2UU4KHduWwNdITnKV\nEjDZ8WW9IWlQRMwAiIjlkg4gGZdmpxxtd/T4jyStFxErSb7kgGw4+Dxfqm06PiI+BX4n6Zb03zfI\n+X1cSWwule5qeGq7E8ngWXs1sOx6x5eNrejKNccn97PUU96DkkuU22t8PXGVXL3Y7Nj6Jp9oNjOz\nTKdqd8DMzNYeTgpmZpZxUrB2R1JI+k3J/HclndPEOpbnWOdRSUMqXac5bZsVxUnB2qMPgf8nqUe1\nO2LW1jgpWHu0iuT5FP9Rd4GkPpL+KmmmpIcl9UrL+0p6UtJzks4tWX+4pHtK5v8g6dh66h2Zxj8t\n6RZJG9SzznJJ50l6VtIUJY9ObbDtdNn3JE1N+/vTtGzXdL6bpPUlzZK0Y7PfLbMSTgrWXl0EHJle\nN17qf0geQLQzyUOKxqflFwJ/jIidSO4OzS3dI/khyThQu5Dc3fuf9ay6PslorgOBvwEnlGtb0kig\nH8kQCoOAwZL+JSKmkgxVfi7J+FPXRsTzTemzWUOcFKxdiuQO06tJhhQvtTtwffr6GpIn3QHsCdxQ\nUt4Uw0gGZHtC0gzgGKB3Pet9BNTudUwnGR6iXNsj0+kZkmERtidJEgA/IxlnZwhJYjBrEb6j2dqz\n35N8meYdGK++m3ZWsfqPp271rCPgwYg4vJH6P47Pbgz6hNU/f/W1LeAXEfGnepZ1JxkdtUvapxWN\ntG2Wi/cUrN2KiKXAzcC3S4onA4elr48E/jd9/USd8lqvAP0lrSNpE5KhouuaAuwp6YsA6XH+LzWh\nqw21fT/wb7XnJyRtLWmLdNmfgB+RHAK7oAltmZXlpGDt3W9Ihh6odRpwXDoY3lEkA4uR/nuKpOeA\nrWtXjogFJInl+fTfZ+o2EBFvkjxc6Ia03idJDvXk1VDbD5Ac6noyXXYrsKGko0n2Oq4neRzqrpK+\n2oT2zBrkYS7MzCzjPQUzM8s4KZiZWcZJwczMMk4KZmaWcVIwM7OMk4KZmWWcFMzMLOOkYGZmmf8P\nLTKuT2nt6dMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d28f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VzLe6mrV+7imYmVnGRcHMzDIuCmZmlnFRMDOzjC80W6vmi+Nm9eOegpmZZVwUzMws46Jg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16351828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xwPFF664tM+ZlQPf0+WBgHsmNi14v5/2T3BjpfOALBW1fSNseLjPH1cBrRY9P\n03/nlRnzWeAw4DiSs66Gp+0HAZPLjHln+vM8kuTOgHcCG9b+npUZc2bB88eBPdLnOwLT/Pm0yc+o\nan6ea71OSwWq5INkxPN94E/AoLStrB98QcwXSa4U7AosAzZP2zsCs5sRtxp+oeeUs66RmOeRXN+x\na0Hba838jJ4veP5GfeuaGPOFouULgL8A3Zrx+cwG2qfPp9T32fnzaVOfUdX8PIsfbeLitYhYDfxc\n0u3pv2/T/Avzfge8THIr0QuA29Mh5VCS+UfKtUNEHJU+v1vSBcBjko5oRsz2ktpHxCpgo4iYChAR\nr0jasIx4r0v6D+DGSCcAVDIx4Ml8fh1Ek0TETyXdRvL5LAAuApp7QGulpIOBTYGQdGRE3J0O98s9\n82pDSe3S3yki4seSFgFPAp3LjHktMFHSZcCDkq4C/ggcSDIVSVOtz58PVMFnVGU/zzW1VHVpTQ+S\nYwGXtkCcrYGt0+ebAcOBIc2MORtoV9R2Msl0FK+XGfNs4GGSX+CLgatIdp39NzCujHhdgctJiuIH\nwPtp3peTjpia+TM4ApgC/L2ZcQaS7Ep5AOifvu8P05/lXmXGvAL4lzraD6V58/UcANwGPA/MJLk/\n+UjqmMumjM/ng/TzuaKFPp9hLfT5DKrj8/kg/Xz2bkbcVv8ZFcXN8/e92T/P4kebPfuotZJ0Bcl+\n30eK2g8FfhkRfcuMewDwHZJdRu1JvjHeDYyNZATR1Hj9gZ4kQ+mPCvOMMq6QLoi5DckB+89IRk0v\nNjPmTiTF+5kWzHMIEBExVdIAkj82L0fzrmQvjLlzGnN2c2IWxR8XESe0RKw03kbATRFxdEvFTOO2\naJ5pzH1ILtx8MSIebqGY+6YxZ5YTU9KeJJ/vUkmdSL6sfRl4juQL65IyY74cEUvSz+c/05izyo1Z\n5+u4KLQekk6JiOsrHVPSOcCZJN8+BwHnRsQ96bq/RkSTpw7PMeZ3Sb4xt1TMi0gO5rUnOUa1J8lx\nmq8AD0XEj1sg5hCSKdnLiqm6z6g7EHgMICKavCuyWmKmcZ+NiCHp89NJfq/uAg4GJkREkyeVLIp5\nWhrz7nJjSpoFDIyIVZLGAMtJjh8elLZ/o4wcWzxmnVpqyOFH8x8UHTyqVEySoXPn9HlvYBrJH1wo\n/wBuNcWsAToBS4FN0vaNKPOss5aOST5n2+UR8/mWjln82QJTgR7p840p46BwHjEpOBmFooPfFB0o\nr2TMuh4tU0EoAAAEfElEQVRt4kBzNZE0o75VwJatJGa7SHfFRMT8dNfUHZK2o/yruasl5qpIpgf5\nWNLfImJpGn+FpHJnsW3pmIOBc0lOgPhhRLwgaUVEPFFmfnnF3D2HmADtJHUlOeuwJiLeBYiI5ZKa\nvKs0p5gvFozSp0saHBHTJO1IcmpqOfKIuRYXhXVvS+AQkgNEhQQ83Upivi1pUES8ABARH0k6HBgL\n7FpmjtUS8xNJnSLiY5I/akBywRjlT23eojEjh7PtqiVmalOSffMiOQtnq4h4S1Jnyv8y0NIxTwOu\nkvRfwHvA5PQspAXpunLkEXMtPqawjkn6HXB9REyqY92tEXF8HZut05iSepJ8u/17Hev2joi/lJFj\ntcTcMCL+UUd7d2CriJjZGmIWxfkaydknP2pOnGqMWRS/E7BlRLzWWmJK2gToQ1IMF0bL3De+xWOu\nEd9FwczMarWJuY/MzKxluCiYmVnGRcGqlqTPJL0gaZak6ZLOU3IHutoZJa/O+fWPTC9ua+p2J0sK\nSf9SFCskDW9k2/8p3M6spfnsI6tmKyJiECRz1QO3ksxCe1FETCO5biFPRwL3AS+VuoGk2v9zM0nu\n11F7ZftxwPTGto+IC5uSoKSa8C1ZrQk8UrA2IZKbl4wEzlLiAKV3nZM0RNJkSc9LelpSv7T9ZEl3\nK5mXfr6ksyT9e9pvitJ57yXtIOlBSc9JekpSf0l7kcxpc2U6Wtmhrn7p9jdIGi3pGZJ5ewCeAoZI\n6pCe9vhFCiZek3ShknnyX5Q0RpIKYg1Pnx+U5jpT0lilkx+m7+VySX8luQ+IWclcFKzNiIh5JFcO\nb1G06mVg34j4EnAhcGnBul2Ab5DcPOnHwMdpv8lA7Z37xgBnR8TuwA9I7nvxNMnU5z+MiEER8be6\n+hW8Tk+SSfr+vTZdklHCISQT0BVPCfGriNgjInYhufL58MKVkjoCNwDHRMSuJKP+7xR0WRwRX46I\n5szoa+sh7z6y9cGmwI2S+pL8Me5QsO7xiFgGLJO0BJiQts8Edku/xe9FMnV67TZrTUdeQr/b69iN\nMx44J83vPKDw/P1/VjI9didgc5JJzyYUrO9HMj//K+nyjSTz9fwiXb6tjp+DWaNcFKzNkLQ9yeyr\n7wA7Fay6hOSP/79K6k0yGV2twovKVhcsryb5/9EO+LD22EUDGuu3vLghIp6VtCvJ6OSV2mKSjgKu\nBQZHxAJJF5Pc3Kkp1no9s1J495G1CZJ6AKNJdrsUX5G5KbAofX5yU+Km8xS9Juno9HUkaWC6ehnQ\npYR+DRnFmiME+LwAvJeOQOo6I2kO0FvSF9PlE4Dmzilk5qJgVW2j2lNSSfbPP0xyY6FiVwA/kfQ8\n5Y2ORwCnSppOshtnWNo+HvhherB3hwb61SsiHoiIx4vaPgSuI7kl7EMks3YWdYmVwCkku6tmkoxs\nRpfx3szW4GkuzKqIpAnAz4oLiVlL8UjBrEpIGkty4HmtiQ/NWopHCmZmlvFIwczMMi4KZmaWcVEw\nM7OMi4KZmWVcFMzMLOOiYGZmmf8P1EqMxsD6Um0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1452a080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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178o2dmkrXtI2zgdLHE/WHpDdVfruiBhftTep1smJWr+ASUDnfPpM4AXgd2Qn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\nZlb4/770/iRZVhWsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11901208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba4cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MQUFERGIKCiIiElNQEBGRmIKCiIjENCS1CSiHoW+5VNJFd5X0XqXhmvr3RHsKIiISU1AQ\nEZGYgoKIiMR0TkGkCMr5vE9T1ZjH5vX5bac9BRERiWlPoZmqpF8+5f5eG9K+xnoedbH7rhye5V3M\nMpVIewoiIhJTUBARkZiCgoiIxBQUREQkphPNddDJqeJr6rcBEGnOtKcgIiIx7SmkoLF+CesXd9PW\nVIeDSvOmPQUREYkpKIiISKzgoGBmrc3soDQbIyIipVVQUDCzLwBzgMei5cPM7KE0GyYiIo2v0D2F\nK4HBwPsA7j4H6JFSm0REpEQKDQofu/u6jDQvdmNERKS0Ch2SusDMzgaqzKwnMAaYml6zpBQ0pFFK\nobHuCCuFKXRP4RKgN7AZ+BPwAXBpWo0SEZHSKGhPwd0/Ar4fTSIi0kwVFBTM7GFqn0NYB8wEfufu\nm3KUGwZcB1QBv3f3a7KsMwT4DVANvOPuxxbcehERKapCDx+9BqwHbommD4APgQOj5VrMrAq4ETgZ\n6AWcZWa9MtbZE/gtcKq79wa+1ID3ICIiRVLoieaj3X1QYvlhM5vh7oPMbEGOMoOBJe7+GoCZTQRO\nAxYm1jkbmOTubwK4++r6NV8aQieUpS46YVu5Ct1TaGtmXWsWovm20eKWHGU6A8sSy8ujtKQDgb3M\n7Fkzm2Vm5xTYHhERSUGhewpjgefN7F+AES5c+6aZ7QbcsZOvPwAYCrQGppnZdHd/NbmSmY0CRgF0\n7dq1ViUiIlIchY4+mhxdn3BwlPRK4uTyb3IUWwHsn1juEqUlLQfWuvsGYIOZPQf0A3YICu4+HhgP\nMHDgQF00JyKSkvrcJbUncBBho/3lAg71zAB6mlkPM9sFOBPIvF/Sg8AxZtbSzNoARwCL6tEmEREp\nokKHpF4BDCGMIppMGFH0PDAhVxl332pmFwOPE4ak3uruC8zsoij/ZndfZGaPAfOATwjDVufvxPsR\nEZGdUOg5heGEPYTZ7n6eme0L3FVXIXefTAgiybSbM5avBa4tsB0iIpKiQg8fbXT3T4CtZtYOWM2O\n5wtERKQZKHRPYWZ0odktwCzChWzTUmuViIiURKGjj74Zzd4cnQNo5+7z0muWiDQnumCy6Sj0yWtP\n18y7+1J3n5dMExGR5iHvnoKZtQLaAB3MbC/ChWsA7ah9dbKIiDRxdR0+upDw3IT9COcSaoLCB8AN\nKbZLRERKIG9QcPfrgOvM7BJ3v76R2iQiIiVS6Inm683saKB7soy757x4TUREmp5Cr2i+EzgAmANs\ni5KdPFc0i4hI01PodQoDgV7u3qRvRqdhcSIi+RV6RfN84FNpNkREREqv0D2FDsBCM3sR2FyT6O6n\nptIqEREpiUKDwpVpNkJERMpDoaOP/mZm3YCe7v5U9OyDqnSbJiIija3Q21xcANwH/C5K6gw8kFaj\nRESkNAo90Twa+DfClcy4+2Jgn7QaJSIipVFoUNjs7ltqFsysJeE6BRERaUYKDQp/M7PvAa3N7ETg\nXuDh9JolIiKlUGhQuBxYA7xMuEneZOAHaTVKRERKo9Ahqa2BW939FgAzq4rSPkqrYSIi0vgK3VN4\nmhAEarQGnip+c0REpJQKDQqt3H19zUI03yadJomISKkUGhQ2mFn/mgUzGwBsTKdJIiJSKoWeU/gW\ncK+ZrSQ8fe1TwIjUWiUiIiVRZ1AwsxbALsDBwEFR8ivu/nGaDRMRkcZXZ1Bw90/M7EZ3P5xwC20R\nEWmmCh59ZGZfNDNLtTUiIlJShQaFCwlXMW8xsw/M7EMz+yDFdomISAkUeuvs3dNuiIiIlF6ht842\nM/uqmf0wWt7fzAan2zQREWlshR4++i1wFHB2tLweuDGVFomISMkUep3CEe7e38xmA7j7e2a2S4rt\nEhGREih0T+Hj6CZ4DmBmHYFPUmuViIiURKFBYRxwP7CPmf0UeB74WV2FzGyYmb1iZkvM7PI86w0y\ns61mNrzA9oiISAoKHX10t5nNAoYSbnNxursvylcm2rO4ETgRWA7MMLOH3H1hlvV+DjzRgPaLiEgR\n5Q0KZtYKuAj4DOEBO79z960F1j0YWOLur0V1TQROAxZmrHcJ8BdgUD3aLSIiKajr8NEdwEBCQDgZ\n+EU96u4MLEssL4/SYmbWGTgDuKke9YqISErqOnzUy937AJjZH4AXi/z6vwEui+6vlHMlMxsFjALo\n2rVrkZsgIiI16goK8Z1Q3X1rPW99tALYP7HcJUpLGghMjOrtAJxiZlvd/YHkSu4+HhgPMHDgQK9P\nI0REpHB1BYV+iXscGdA6WjbA3b1dnrIzgJ5m1oMQDM5k+8VvECroUTNvZrcDj2QGBBERaTx5g4K7\nVzW04mjP4mLgcaAKuNXdF5jZRVH+zQ2tW0RE0lHoFc0N4u6TgckZaVmDgbufm2ZbRESkboVevCYi\nIhVAQUFERGIKCiIiElNQEBGRmIKCiIjEFBRERCSmoCAiIjEFBRERiSkoiIhITEFBRERiCgoiIhJT\nUBARkZiCgoiIxBQUREQkpqAgIiIxBQUREYkpKIiISExBQUREYgoKIiISU1AQEZGYgoKIiMQUFERE\nJKagICIiMQUFERGJKSiIiEhMQUFERGIKCiIiElNQEBGRmIKCiIjEFBRERCSmoCAiIjEFBRERiaUa\nFMxsmJm9YmZLzOzyLPlfMbN5ZvaymU01s35ptkdERPJLLSiYWRVwI3Ay0As4y8x6Zaz2OnCsu/cB\nfgKMT6s9IiJStzT3FAYDS9z9NXffAkwETkuu4O5T3f29aHE60CXF9oiISB3SDAqdgWWJ5eVRWi5f\nBx5NsT0iIlKHlqVuAICZHUcICsfkyB8FjALo2rVrI7ZMRKSypLmnsALYP7HcJUrbgZn1BX4PnObu\na7NV5O7j3X2guw/s2LFjKo0VEZF0g8IMoKeZ9TCzXYAzgYeSK5hZV2AS8DV3fzXFtoiISAFSO3zk\n7lvN7GLgcaAKuNXdF5jZRVH+zcCPgPbAb80MYKu7D0yrTSIikl+q5xTcfTIwOSPt5sT8+cD5abZB\nREQKpyuaRUQkpqAgIiIxBQUREYkpKIiISExBQUREYgoKIiISU1AQEZGYgoKIiMQUFEREJKagICIi\nMQUFERGJKSiIiEhMQUFERGIKCiIiElNQEBGRmIKCiIjEFBRERCSmoCAiIjEFBRERiSkoiIhITEFB\nRERiCgoiIhJTUBARkZiCgoiIxBQUREQkpqAgIiIxBQUREYkpKIiISExBQUREYgoKIiISU1AQEZGY\ngoKIiMQUFEREJJZqUDCzYWb2ipktMbPLs+SbmY2L8ueZWf802yMiIvmlFhTMrAq4ETgZ6AWcZWa9\nMlY7GegZTaOAm9Jqj4iI1C3NPYXBwBJ3f83dtwATgdMy1jkNmODBdGBPM+uUYptERCQPc/d0KjYb\nDgxz9/Oj5a8BR7j7xYl1HgGucffno+WngcvcfWZGXaMIexIABwGvRPMdgHdyNKEheeVcphzaUEnt\nrqT3Wg5tqKR2l+q9dnP3jjnW287dU5mA4cDvE8tfA27IWOcR4JjE8tPAwHq8xsxi5pVzmXJoQyW1\nu5Leazm0oZLaXQ7vNd+U5uGjFcD+ieUuUVp91xERkUaSZlCYAfQ0sx5mtgtwJvBQxjoPAedEo5CO\nBNa5+1sptklERPJomVbF7r7VzC4GHgeqgFvdfYGZXRTl3wxMBk4BlgAfAefV82XGFzmvnMuUQxsq\nqd2V9F7LoQ2V1O5yeK85pXaiWUREmh5d0SwiIjEFBRERiSkoiIhITEFBRERiqY0+SouZ7Qt0jhZX\nuPuqnSmTK68hZYpdX0PeayUxs4MJt0qJ+4gwzNmzpbv7oqZYphzaUEntLucyO5NHgZrM6CMzOwy4\nGdiD7Re4dQHeB77p7i9lbkSBTnnK/Aa4NEvelmi+uh5lil1fvjLfJAzfreh/auBU4CzCPbWWJ/po\nTDQ/LiP9TOAtwneiKZUphzZUUrvLuczO5E1092soQFMKCnOAC939HxnpRwJ3AO9Re+O6P/ANd5+Q\npcwUYEiW+l4l9EvPepQpdn35ykwCVpP+P2E5/BPkK9MZ2M/dP87oo1x9twuwHtitiZUphzZUUrvL\nuczO5C3IfK2c6ntfjFJNwOI8eZsJN9vLTF8GzM1RZkuu1yHc3bXgMsWur64yQHWW9F3y5L2arf/q\nKFPs+tIo0y1L3hLgX1nSu0Xfk6ZWphzaUEntLucyO5P3SmZ6rqkpnVN41Mz+CkwgbOwh7AmcA2z0\njF/bkfuBr5vZiCxlFuWoryXh+T/1KVPs+vKV2QDsB7yR8V47EQ61ZMtrAViW/slXptj1FbvMKuBp\nM1vM9j7qCrQBMLNHM9I/A/y4CZYphzZUUrvLuczO5MV3p65Lkzl8BGBmJ5P9+PIw4ACyb6y3ASsz\ny7j75Dz15Tr2nbNMsevLVQb4BLiBsDeR+cHfRrhVSGZe32h+bj3KFLu+Ype5GHiC8NyOZB/NiPqu\nVrq7bzOzFk2tTDm0oZLaXc5ldiaPAjWpoJBPvo116VqVjsb6JyyHf4J8ZerXayJSkEKPM5XzBIwq\nZplceQ0pU+z6GvJeK20CHqlPelMtUw5tqKR2l3OZncmrtW6hK5bzRBiVlCsv18Y1X5mseQ0pU+z6\n6ihT8i9gmZTpVJ/0plqmHNpQSe0u5zI7k5c5NanDRw25MMPMfgS8APzD3dcn0ocB7wLu7jPMrBfh\n3MQ/PeOQk5lNcPdzstR9DOHQxnxgHbDI3T8ws9bA5UB/oDUwxt0XZilf85yJle7+lJmdDRwNLAKe\nJIzF359wXuRV4I/u/kGe99rJczyPIldeQ8oUu75il0mbme3j7qvrWaa9u69Nq02NrSF9EJWr+H4o\n+z4oNHqUegIuA+YQNrZfjabLa9JylBlDGAP/ALAUOC2RtxKYDswErgaeAX4IrCVslB+KpocJ438f\nAt5LlL8geu0rCEFnFdAyyhtPuDDtGGATsBH4O+HCs46JOu4G7ole407CaKmvAf8gBLwfAFOBG4Gf\nAgsJ1zWU8nPYpwFl2qfQjj2Aa4B/EoJ7zed2DbBnjjJPRJ/1ncDZifRPEQL7jUB74ErgZeDPwCHA\n3ompffRdGg7snWjLH4B5wB8J11V0iPIGAq8RhiBuBn4PHJClbQMJ163cRfgh8CThh8YMwg+FHwML\norQ10Xf3Gw3og3bAvzL7IMq7DbgpSz88kNEPNX2wFzA84zNJqx9mEf6vMvvgXH0XGt4PWfumlBuY\nem4EXiX3ePqs1zBEH+ayaL47IQB8K1reSHj4TxvgA6BdlD6bcCHcEODY6O9b0fziRN0ziDbwwG7A\npkTeS4n52YTgcVL0ZVkDPAaMBOZH67QkBJWqRLvnRfNtgGej+a6EkTiNsSEoh3+CfBuCxwk/FD6V\n8Q99HTCNsJeWnAZEn/k1wOmEIP8XYNfo81hG+JExL6p3f+ASwonu1zOmj6P381r0ur8HriKMB/9v\nwhMEa9o0BRgUzS8D3gbeBF6M1t0vynsROJlwlfYyoo0tMDT6nM8lXLj3P4QfLz0JPxyeydIHl+Xo\ng/6E56Cvz+yDqOy66D1n9sMnhKHQmX3wOrA58dpp9sMLhO9RZh/cQfiOVfp3IV8/XAY80RyDwj/J\nfWHGpugDzJw2ZXxp20Yf+q+Aj5Ib7sR8C8I/25PAYVFazQc+l/DrqD2JDX+U9x5wXmJDOzCaX0AY\nLVOzXjXhsNCfgK2EoLYX8CHbN7TzCYexiPJmJsp/mOeDL+aGoBz+CfJtCNbm+J5sI2zApmSZPslY\n9/vRa8yr+TyBNzPWWRF9Z/ok0l5nx8A/J6PMJrbvNU5PpL8EvBzNfxb4bdQnU5Kvm6UNGzOWZ0R/\nX6n5nmTpBycEjMw++DBZX6IP2rPj/0SyPWMJP5x26IOa99QY/UD435udpQ9akPgfr+DvQs5+qPmu\n5MqrtW6hK5Z6IhzvXwI8Svj1OD76gJYQNsiHETZMyWkqsDqjnpaE6xkcaFPToYn8PaIPrAtwL+Ga\ngDejvKWEaPx69LdTlN42+jLdTvhF/g/CxvM1wj9hvxzv6X+jdd4gHOp6GriFcGjr7Wj+n2wPNh1J\n/ONmqa9oG4Jy+Ccg/4ZgQ9R/+yby943qfCFH/3yc/KyjtHOjNr8RLV+Vkf9y4rvwK2D36DNbTghU\nY6M+sYzP0E1ZAAAGyklEQVR+ewI4nrDndR1hT/Mt4M6M+qsI3+3VhL3JL0Xfh9Oj/GMJAf2YaPlU\n4PFo/omoXGYfXBaV6ZmlDxYR7T1n9MECElfYZ+mHRZl9EKU3Vj8kfyjFfRAtN9Z3YV65fhfq6IfL\ngKcK3tYWumI5TISNwZHAF6PpyKgj/1DTURnrdwEm5ahrSI70Duy4Efw88LM62tUG6BHNtwP6EXZR\n9wUOrKPsfmz/xbwn4dDMYKB3NH9wxvpP5Pngi70hKPUGMd+GYDHwc0LQfI+wW72IcPx7cI6+ngSc\nkCX9brLcVoRwkdx9ieVTCYev3iacS0pONYcSP0X40TGEcL5odtSPkwmHHGsdAo3K9SMcEnsUODjq\nt/ejz+gcwt7Ue8DzwEGJ9j2VpQ9+Hn2+B2V5nf8DfpQlfRjhUGTbfP2Q7INouTH64T3Cj7+FiT44\nMFq/I+G7n/wuvNcI34XT6vFdOK6efXBYjj5YQDjs/GL03Uh+F7L1Q/L7sHe+7dAOr1/oiprKYyIc\nTqr54N/N+OBHprEhiJaLsUFsmeM95dog5tsQjInWPSGz/cD5hOOvmenDojLZ8i4opAxhNNmhO/E6\n+cocUkdetvc6hu2H6HoTAvUp0fLgRF4vQiA/JVd6Pcr0IQyCyFsmS17cvjrKHJGjzBG5ymT5Tt2Z\nI31Cnv+trHl50lsD96b9OvneTx31fTbqu5Nylc02NakhqZKfmZ3n7rfVJ68+ZaKhtge4+/xi1LeT\nZe4mnKBeRPhl9S13f9DMxhCC4GPJ9KjMMsJtxzPLXAJcWwZlNhCCfUF5ZnYFYa+xZgjzYOBZ4ETC\nDQPbEg6XPknYoE4Bvk7Y416dkV6fMvlep5C8+rShkDIdCXuOSccT9pwh/LKGcB+t4wiHWAcn0pN5\n9SmT63Vq0utTppDXqSvvs+6+F4CZnQ+MJuwtnQQ87AXeOrvkv3w1FW8i45xAIXkNKVPs+hpYZgvR\nr2YSI8sIeyZzM9Oj5Y1NrUwB9c2m9gi61uQeXTefcGw87TKN3Ya7qD1icDHhUGZm+rFRXn3LvNpI\nZXK1rc68xP9H5ujIlwvejpR6Q6apfhPZR1nNI2wgPsmRtzFHXr4yxa6v6GUy+qVmZNk7JE5+s+OI\ns8yRG02hTL681TX1kTghHy3nGl03uzHKNHIb5hBGs+0wYpCwZ1ErPfqbNa+cyxSQl2905A59lncb\nU+qNnKb6TYTrGbKNtOpOGIKXLW8NYQNSnzLFrq/YZTbX/FMk+qYl4VfTtizpNSPOmlqZfHlrauqj\n9gi6DWQfXTeTaAORcpnGbEPNENJaIwbzpTfVMrnyyD86codRgvmmpvQ8BQkeIRxKmJOZYWZLs+WZ\n2UNAV3fPfF5BzjLFri+FMpMJJ71j7r7VzAYBh2emA+eY2aSmVqaO+roQzqvg7p8ksqsJx5c/ypJ3\nKuGXZNplGrMNI6P05cCXzOzzhENM5EtvqmVy5bl7d7L7BDgjR14tOtEsIiKxFqVugIiIlA8FBRER\niSkoSEUxs21mNsfM5pvZvWbWpkTtuDT52mY22cz2jObX5y4pki4FBak0G939MHc/lHCtw0WFFjSz\nqiK241Kih7cDuPsp7v5+EesXaRAFBalkfyfczgMz+6qZvRjtRfyuJgCY2Xoz+6WZzQWOMrNBZjbV\nzOZG6+9uZlVmdq2ZzTCzeWZ2YVR2iJk9a2b3mdk/zexuC8YQ7nk1xcymROsuNbMOmQ00s+8k6v1/\njdUxUrkUFKQimVlLwu26XzazQ4ARwL+5+2GE6ya+Eq26G+Gpff0Itxe4h3ClcT/CvYg2Em7BsM7d\nBwGDgAvMrEdU/nDCXkEv4NPRa4wj3An3OHc/Lk8bTyLcKnww4TqOAWb2uWL1gUg2uk5BKk1rM6u5\nHuLvhDvsjiLc1XaGmUG4dULNIxa3EZ4/AXAQ8Ja7zwDw6NGo0ca7r5kNj9bbg7Ax3wK8GI0pJ3rd\n7oSb+xXipGiaHS23jep9rvC3K1I/CgpSaTZGewMxC5HgDnf/bpb1N7n7tjrqNOASd388o94hhCuv\na2yjfv9zBlzt7r+rRxmRnaLDRyLh4UbDzWwfADPb28y6ZVnvFaBTdGUy0fmEloTbfn/DzKqj9APN\nbLc6XvNDwvMp8nkc+C8zaxvV27mmjSJp0Z6CVDx3X2hmPwCeMLMWhKdyjSY88Ce53hYzGwFcH91G\nfCPhvMLvCYeFXor2OtYQHn2az3jgMTNbmeu8grs/EZ3vmBYd1loPfJXth7ZEik63uRARkZgOH4mI\nSExBQUREYgoKIiISU1AQEZGYgoKIiMQUFEREJKagICIiMQUFERGJ/X+oo2sTrJX62wAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16347630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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byPzgbyNcKiSzrmf0fHod2hS7v2K3uQB4knDfjuQYTY7Gbotyd99oZs0aW5ty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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a7e208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G1GU+Kmr3UUNOzDCzXwCvAH9391WJ8oHAp4C7+2Qz60Y4NvGWZ+xyMrP73X1I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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11161e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CSUFERBI60CxSBpUe5itSLkoKLUwrj+qwNg7nrfQQaVk7aPeRiIgklBRERCSRaVIwsyFm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\nICIiCSUFERFJ/H+i6zd5Cufx2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11898470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9hVjWZYThw2Q/TXf3bWbWhPBsj5zzCP2eV5lyrq+c2676cquPPJRVUMgm20a3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\nmS3KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ae2d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zGrtNyt19g5m1amptKiGGlhR3JbfZnDpSalJJoZhiK+vyRZWNxvonrIR/gmJt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\nJQUREYn9f6rUequX43lXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x162bde10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in featureslist:\n",
    "    dist = pd.crosstab(roundedfeatures[feature],np.array(malignantlabel))\n",
    "    dist.div(dist.sum(1).astype(float), axis=0).plot(kind='bar',stacked=True)\n",
    "    plt.ylabel(\"Percentage\")\n",
    "    plt.legend([\"Non-cancer\",\"Cancer\"])\n",
    "    plt.title(feature)\n",
    "    plt.savefig(\"stacked\"+str(feature)+\".png\",dpi=150)\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "for feature in featureslist:\n",
    "    dist = pd.crosstab(percentilefeatures[feature],np.array(malignantlabel))\n",
    "    dist.div(dist.sum(1).astype(float), axis=0).plot(kind='bar',stacked=True)\n",
    "    plt.xlabel(\"Percentile\")\n",
    "    plt.ylabel(\"Percentage\")\n",
    "    plt.legend([\"Non-cancer\",\"Cancer\"])\n",
    "    plt.title(feature)\n",
    "    plt.show()\n",
    "\n",
    "#shift MeanHU values so the lowest value is 0\n",
    "roundedfeatures[featureslist[0]]=roundedfeatures[featureslist[0]].values-min(roundedfeatures[featureslist[0]].values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model selection, evaluation, and optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance of features discretized by transforming to percentiles and rounding, multinomial naive bayes\n",
      "[ 0.58059024  0.58059024  0.57812448  0.57812448  0.57812448]\n",
      "Cross-validated logloss 0.579110784073\n",
      "Performance of features discretized by rounding, multinomial naive bayes.\n",
      "[ 0.56471945  0.56237559  0.54337234  0.54241964  0.54991449]\n",
      "Cross-validated logloss 0.55256030172\n"
     ]
    }
   ],
   "source": [
    "#Naive bayes may work better if discretized into categories.\n",
    "#Compare features discretized by transforming into percentiles vs rounded\n",
    "\n",
    "featureslist=[\"Diameter\"]\n",
    "clf=MultinomialNB()\n",
    "scores=cross_val_score(clf,percentilefeatures[featureslist], malignantlabel, cv=StratifiedKFold(n_splits=5, shuffle=True), n_jobs=-1, scoring='neg_log_loss')\n",
    "print(\"Performance of features discretized by transforming to percentiles and rounding, multinomial naive bayes\")\n",
    "print(-scores)\n",
    "print(\"Cross-validated logloss\",-np.mean(scores))\n",
    "\n",
    "featureslist=[\"Diameter\",\"Spiculation\",\"MeanHU\",\"Eccentricity\"]\n",
    "clf=MultinomialNB()\n",
    "print(\"Performance of features discretized by rounding, multinomial naive bayes.\")\n",
    "scores=cross_val_score(clf,roundedfeatures[featureslist], malignantlabel, cv=StratifiedKFold(n_splits=5, shuffle=True), n_jobs=-1, scoring='neg_log_loss')\n",
    "print(-scores)\n",
    "print(\"Cross-validated logloss\",-np.mean(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes\n",
      "Average precision score: 0.414522511619\n",
      "Area under curve: 0.638017121599\n",
      "Cross-validated logloss 0.585657323734\n",
      "---------------------------------------\n",
      "Multinomial Naive Bayes\n",
      "Average precision score: 0.409703998488\n",
      "Area under curve: 0.645878714535\n",
      "Cross-validated logloss 0.744464855609\n",
      "---------------------------------------\n",
      "Logistic Regression\n",
      "Average precision score: 0.413949770137\n",
      "Area under curve: 0.645074178656\n",
      "Cross-validated logloss 0.552311390514\n",
      "---------------------------------------\n",
      "Random Forest\n",
      "Average precision score: 0.311568121202\n",
      "Area under curve: 0.559712697772\n",
      "Cross-validated logloss 1.53144104481\n",
      "---------------------------------------\n",
      "Gradient Boosting\n",
      "Average precision score: 0.351506917541\n",
      "Area under curve: 0.589460355878\n",
      "Cross-validated logloss 0.580872393613\n",
      "---------------------------------------\n",
      "SVM with rbf kernel\n",
      "Average precision score: 0.260709649793\n",
      "Area under curve: 0.488246828918\n",
      "Cross-validated logloss 0.579591160447\n",
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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jN2YMzq1bV+taeRnpbF3xPXt+XWfZ98B7n+AX0rBmhb8CqFBQFKVmMo/BnmWAhP2rAIj0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\nw4Y0bdqUnTt35qjO2bNns2jRIgBmzpzJ4cOHs1XP21xK//33X8zMzHB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jk9x+nERSoERE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\nFCQSiUSiQYqCRCKRSDRIUZBIJBKJhv8DjWtJsAOaWuUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118a1908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_splits=24\n",
    "kfold=StratifiedKFold(n_splits=n_splits, shuffle=False)\n",
    "\n",
    "featurelist=[\"Diameter\",\"MeanHU\",\"Spiculation\",\"Eccentricity\"]\n",
    "models=[GaussianNB(), MultinomialNB(),\n",
    "    LogisticRegression(C=0.1),\n",
    "        RandomForestClassifier(),\n",
    "        GradientBoostingClassifier(),\n",
    "    SVC(C=0.02,kernel='rbf', probability=True),\n",
    "        SVC(C=0.02, kernel='linear', probability=True)]\n",
    "name=[\"Gaussian Naive Bayes\", \"Multinomial Naive Bayes\", \"Logistic Regression\", \"Random Forest\", \"Gradient Boosting\", \"SVM with rbf kernel\", \"SVM with linear kernel\"]\n",
    "\n",
    "predictedmodels={}\n",
    "\n",
    "for nm, clf in zip(name[:-1], models[:-1]):\n",
    "    print(nm)\n",
    "    predicted=[]\n",
    "    mallabelcv=[]\n",
    "    for train,test in kfold.split(inputfeatures,malignantlabel):\n",
    "        if nm==name[1]:\n",
    "            clf.fit(roundedfeatures[featurelist].iloc[train],[malignantlabel[i] for i in train])\n",
    "            predicted.append(clf.predict_proba(roundedfeatures[featurelist].iloc[test])[:,1])\n",
    "        else:\n",
    "            clf.fit(inputfeatures[featurelist].iloc[train],[malignantlabel[i] for i in train])\n",
    "            predicted.append(clf.predict_proba(inputfeatures[featurelist].iloc[test])[:,1])\n",
    "        mallabelcv.append([malignantlabel[i] for i in test])\n",
    "    if nm==name[1]:        \n",
    "        scores=cross_val_score(clf,roundedfeatures[featurelist], malignantlabel, cv=StratifiedKFold(n_splits=n_splits, shuffle=True), n_jobs=-1, scoring='neg_log_loss')\n",
    "    else:\n",
    "        scores=cross_val_score(clf,inputfeatures[featurelist], malignantlabel, cv=StratifiedKFold(n_splits=n_splits, shuffle=True), n_jobs=-1, scoring='neg_log_loss')\n",
    "    predicted=np.concatenate(np.array(predicted),axis=0)\n",
    "    mallabelcv=np.concatenate(np.array(mallabelcv),axis=0)\n",
    "    predictedmodels[nm]=predicted\n",
    "    roc=roc_curve(mallabelcv,predicted)\n",
    "    print(\"Average precision score:\", average_precision_score(mallabelcv,predicted))\n",
    "    print(\"Area under curve:\", auc(roc[0],roc[1]))\n",
    "    plt.plot(roc[0],roc[1])\n",
    "    #print(-scores)\n",
    "    print(\"Cross-validated logloss\",-np.mean(scores))\n",
    "    print(\"---------------------------------------\")\n",
    "    #plt.plot(rocrandom[0],rocrandom[1])\n",
    "plt.title('ROC')\n",
    "plt.ylabel('TPrate')\n",
    "plt.xlabel('FPrate')\n",
    "plt.legend(name)\n",
    "plt.savefig(\"clfroccomparison.png\",dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models averaged: ['Gaussian Naive Bayes', 'Multinomial Naive Bayes', 'Logistic Regression', 'SVM with rbf kernel']\n",
      "Area under curve: 0.639771861414\n",
      "Average precision score: 0.394218031035\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b67630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression logloss 0.554847995102\n",
      "Ensemble of models logloss 0.556005624441\n"
     ]
    }
   ],
   "source": [
    "#Ensemble of multiple models by averaging their prediction outputs\n",
    "\n",
    "predictedmodels=pd.DataFrame(predictedmodels)\n",
    "models=[name[i] for i in [0,1,2,5]]\n",
    "predictedmean=np.mean(predictedmodels[models],axis=1)\n",
    "roc=roc_curve(mallabelcv,predictedmean)\n",
    "print(\"Models averaged:\",models)\n",
    "print(\"Area under curve:\", auc(roc[0],roc[1]))\n",
    "print(\"Average precision score:\", average_precision_score(mallabelcv,predictedmean))\n",
    "plt.plot(roc[0],roc[1])\n",
    "plt.title('ROC')\n",
    "plt.ylabel('TPrate')\n",
    "plt.xlabel('FPrate')\n",
    "plt.show()\n",
    "print(\"Logistic Regression logloss\",log_loss(mallabelcv,predictedmodels[name[2]]))\n",
    "print(\"Ensemble of models logloss\",log_loss(mallabelcv,predictedmean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes\n",
      "Average precision score: 0.264615359245\n",
      "Area under curve: 0.48319454528\n",
      "Cross-validated logloss 0.601300244897\n",
      "---------------------------------------\n",
      "Multinomial Naive Bayes\n",
      "Average precision score: 0.249989475393\n",
      "Area under curve: 0.460893695932\n",
      "Cross-validated logloss 0.590140187634\n",
      "---------------------------------------\n",
      "Logistic Regression\n",
      "Average precision score: 0.243979584085\n",
      "Area under curve: 0.452701993475\n",
      "Cross-validated logloss 0.591648749961\n",
      "---------------------------------------\n",
      "Random Forest\n",
      "Average precision score: 0.267203732376\n",
      "Area under curve: 0.474753549155\n",
      "Cross-validated logloss 1.50520689192\n",
      "---------------------------------------\n",
      "Gradient Boosting\n",
      "Average precision score: 0.267844392757\n",
      "Area under curve: 0.48298789395\n",
      "Cross-validated logloss 0.619576237054\n",
      "---------------------------------------\n",
      "SVM with rbf kernel\n",
      "Average precision score: 0.275422835589\n",
      "Area under curve: 0.493881371203\n",
      "Cross-validated logloss 0.590280065819\n",
      "---------------------------------------\n",
      "SVM with linear kernel\n",
      "Average precision score: 0.291751930655\n",
      "Area under curve: 0.50785275054\n",
      "Cross-validated logloss 0.590642358879\n",
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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MCAwMfGysAN27d2fVqlVKGss7d+4QGxtLVFQUenp6DB48mAkTJnDu3LkyLakN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\nQ2/UWW4fCSGEcJNQEEII4TbYQuG5vi5AH5A6Dw5S58HB63UeVH0KQgg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11876940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Benchmark model with stratified random labels\n",
    "\n",
    "n_splits=24\n",
    "kfold=StratifiedKFold(n_splits=n_splits, shuffle=False)\n",
    "\n",
    "featurelist=[\"Diameter\",\"MeanHU\",\"Spiculation\",\"Eccentricity\"]\n",
    "models=[GaussianNB(), MultinomialNB(),\n",
    "    LogisticRegression(C=0.1),\n",
    "        RandomForestClassifier(),\n",
    "        GradientBoostingClassifier(),\n",
    "    SVC(C=0.02,kernel='rbf', probability=True),\n",
    "        SVC(C=0.02, kernel='linear', probability=True)]\n",
    "name=[\"Gaussian Naive Bayes\", \"Multinomial Naive Bayes\", \"Logistic Regression\", \"Random Forest\", \"Gradient Boosting\", \"SVM with rbf kernel\", \"SVM with linear kernel\"]\n",
    "\n",
    "predictedmodels={}\n",
    "tprmodels={}\n",
    "fprmodels={}\n",
    "\n",
    "\n",
    "for nm, clf in zip(name[:-1], models[:-1]):\n",
    "    print(nm)\n",
    "    predicted=[]\n",
    "    mallabelcv=[]\n",
    "    for train,test in kfold.split(inputfeatures,randomlabel):\n",
    "        if nm==name[1]:\n",
    "            clf.fit(roundedfeatures[featurelist].iloc[train],[randomlabel[i] for i in train])\n",
    "            predicted.append(clf.predict_proba(roundedfeatures[featurelist].iloc[test])[:,1])\n",
    "        else:\n",
    "            clf.fit(inputfeatures[featurelist].iloc[train],[randomlabel[i] for i in train])\n",
    "            predicted.append(clf.predict_proba(inputfeatures[featurelist].iloc[test])[:,1])\n",
    "        mallabelcv.append([randomlabel[i] for i in test])\n",
    "    if nm==name[1]:        \n",
    "        scores=cross_val_score(clf,roundedfeatures[featurelist], randomlabel, cv=StratifiedKFold(n_splits=n_splits, shuffle=True), n_jobs=-1, scoring='neg_log_loss')\n",
    "    else:\n",
    "        scores=cross_val_score(clf,inputfeatures[featurelist], randomlabel, cv=StratifiedKFold(n_splits=n_splits, shuffle=True), n_jobs=-1, scoring='neg_log_loss')\n",
    "    predicted=np.concatenate(np.array(predicted),axis=0)\n",
    "    mallabelcv=np.concatenate(np.array(mallabelcv),axis=0)\n",
    "    predictedmodels[nm]=predicted\n",
    "    roc=roc_curve(mallabelcv,predicted)\n",
    "    print(\"Average precision score:\", average_precision_score(mallabelcv,predicted))\n",
    "    print(\"Area under curve:\", auc(roc[0],roc[1]))\n",
    "    plt.plot(roc[0],roc[1])\n",
    "    #print(-scores)\n",
    "    print(\"Cross-validated logloss\",-np.mean(scores))\n",
    "    print(\"---------------------------------------\")\n",
    "    #plt.plot(rocrandom[0],rocrandom[1])\n",
    "plt.title('ROC')\n",
    "plt.ylabel('TPrate')\n",
    "plt.xlabel('FPrate')\n",
    "plt.legend(name)\n",
    "plt.savefig(\"clfroccomparison.png\",dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=2.9999999999999996, class_weight=None, dual=False,\n",
      "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
      "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
      "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False)\n",
      "['Optimized Logistic Regression']\n",
      "[ 0.58413531  0.54038305  0.5748129   0.52855611  0.54155647]\n",
      "Cross-validated logloss 0.553888769981\n",
      "---------------------------------------\n",
      "[ 0.58393711  0.54044453  0.5747408   0.52868858  0.54150315]\n",
      "Cross-validated logloss 0.553862832778\n",
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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gRQSUFCRcEjuzJv0SPt23h27dE1SMTqSZUlKQoPEVofNJObiO5KJNnkVncWl8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\nKSmIiIifkoKIiPj9f2fgfZIHKXkqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1118e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featureslist=['Diameter','Spiculation','Eccentricity','MeanHU']\n",
    "def fit_model(X, y, clf):\n",
    "    cv_sets = ShuffleSplit(X.shape[0], n_iter = 5, test_size = 0.20, random_state = 42)\n",
    "    params = [{'C':np.arange(0.9,3.1,0.1),'solver':['liblinear'],'penalty':['l1','l2']},\n",
    "              {'C':np.arange(0.01,0.1,0.01),'solver':['newton-cg'],'penalty':['l2']},\n",
    "             {'C':np.arange(0.01,0.1,0.01),'solver':['lbfgs'],'penalty':['l2']},\n",
    "             {'C':np.arange(0.01,0.1,0.01),'solver':['sag'],'penalty':['l2']},\n",
    "             {'C':np.arange(0.01,0.1,0.01),'solver':['saga'],'penalty':['l1']}]\n",
    "    grid = GridSearchCV(clf, params, cv=cv_sets, scoring=\"neg_log_loss\", n_jobs=-1)\n",
    "    grid = grid.fit(X, y)\n",
    "    return grid.best_estimator_\n",
    "\n",
    "optimal_lr=fit_model(inputfeatures[featurelist],malignantlabel,LogisticRegression())\n",
    "\n",
    "print(optimal_lr)\n",
    "\n",
    "name=[\"Optimized Logistic Regression\"]\n",
    "print(name)\n",
    "scores=cross_val_score(optimal_lr,inputfeatures[featurelist], malignantlabel, cv=5, scoring='neg_log_loss')\n",
    "#print(classification_report(Ytest,model.predict(Xtest[featurelist])))\n",
    "print(-scores)\n",
    "print(\"Cross-validated logloss\",-np.mean(scores))\n",
    "print(\"---------------------------------------\")\n",
    "clf=optimal_lr\n",
    "clf.fit(Xtrain[featurelist],Ytrain)\n",
    "roc=roc_curve(Ytest,clf.predict_proba(Xtest[featurelist])[:,1])\n",
    "\n",
    "#ROC curve\n",
    "plt.plot(roc[0],roc[1], alpha=0.5)\n",
    "#plt.plot(rocrandom[0],rocrandom[1])\n",
    "\n",
    "scores=cross_val_score(LogisticRegression(),inputfeatures[featurelist], malignantlabel, cv=5, scoring='neg_log_loss')\n",
    "#print(classification_report(Ytest,model.predict(Xtest[featurelist])))\n",
    "print(-scores)\n",
    "print(\"Cross-validated logloss\",-np.mean(scores))\n",
    "print(\"---------------------------------------\")\n",
    "clf=LogisticRegression()\n",
    "clf.fit(Xtrain[featurelist],Ytrain)\n",
    "roc=roc_curve(Ytest,clf.predict_proba(Xtest[featurelist])[:,1])\n",
    "\n",
    "#ROC curve\n",
    "plt.plot(roc[0],roc[1], alpha=0.5)\n",
    "#plt.plot(rocrandom[0],rocrandom[1])\n",
    "plt.title('ROC')\n",
    "plt.ylabel('TPrate')\n",
    "plt.xlabel('FPrate')\n",
    "plt.legend(['Optimized Logistic Regression', 'Default Logistic Regression'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=15, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "['Optimized Random Forest Classifier']\n",
      "[ 0.60736264  0.5680371   0.57758907  0.54069499  0.54751972]\n",
      "Cross-validated logloss 0.568240704889\n",
      "---------------------------------------\n",
      "[ 1.91837366  1.87828671  1.70397392  1.02801084  0.94509846]\n",
      "Cross-validated logloss 1.49474871942\n",
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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YJl0GG46UEGQLID4yhGF9I4k/f6JZ5SlruGljNQybDf3H6HBT5RWaFJTysS1H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\nSWB50MwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c47240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featureslist=['Diameter','Spiculation','Eccentricity','MeanHU']\n",
    "def fit_model(X, y, clf):\n",
    "    cv_sets = ShuffleSplit(X.shape[0], n_iter = 5, test_size = 0.20, random_state = 42)\n",
    "    params = {'n_estimators':[2,5,7,8,9,10,15],'max_depth':[2,3,4,5,6,7,8,None]}\n",
    "    grid = GridSearchCV(clf, params, cv=cv_sets, scoring=\"neg_log_loss\", n_jobs=-1)\n",
    "    grid = grid.fit(X, y)\n",
    "    return grid.best_estimator_\n",
    "\n",
    "optimal_rf=fit_model(roundedfeatures[featurelist],malignantlabel,RandomForestClassifier())\n",
    "\n",
    "print(optimal_rf)\n",
    "\n",
    "name=[\"Optimized Random Forest Classifier\"]\n",
    "print(name)\n",
    "scores=cross_val_score(optimal_rf,roundedfeatures[featurelist], malignantlabel, cv=5, scoring='neg_log_loss')\n",
    "#print(classification_report(Ytest,model.predict(Xtest[featurelist])))\n",
    "print(-scores)\n",
    "print(\"Cross-validated logloss\",-np.mean(scores))\n",
    "print(\"---------------------------------------\")\n",
    "clf=optimal_rf\n",
    "clf.fit(Xtrain[featurelist],Ytrain)\n",
    "roc=roc_curve(Ytest,clf.predict_proba(Xtest[featurelist])[:,1])\n",
    "\n",
    "#ROC curve\n",
    "plt.plot(roc[0],roc[1], alpha=0.5)\n",
    "#plt.plot(rocrandom[0],rocrandom[1])\n",
    "\n",
    "scores=cross_val_score(RandomForestClassifier(),inputfeatures[featurelist], malignantlabel, cv=5, scoring='neg_log_loss')\n",
    "#print(classification_report(Ytest,model.predict(Xtest[featurelist])))\n",
    "print(-scores)\n",
    "print(\"Cross-validated logloss\",-np.mean(scores))\n",
    "print(\"---------------------------------------\")\n",
    "clf=RandomForestClassifier()\n",
    "clf.fit(Xtrain[featurelist],Ytrain)\n",
    "roc=roc_curve(Ytest,clf.predict_proba(Xtest[featurelist])[:,1])\n",
    "\n",
    "#ROC curve\n",
    "plt.plot(roc[0],roc[1], alpha=0.5)\n",
    "#plt.plot(rocrandom[0],rocrandom[1])\n",
    "plt.title('ROC')\n",
    "plt.ylabel('TPrate')\n",
    "plt.xlabel('FPrate')\n",
    "plt.legend(['Optimized Random Forest', 'Default Random Forest'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
      "              learning_rate=0.01, loss='deviance', max_depth=2,\n",
      "              max_features=None, max_leaf_nodes=7,\n",
      "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "              min_samples_leaf=1, min_samples_split=2,\n",
      "              min_weight_fraction_leaf=0.0, n_estimators=150,\n",
      "              presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
      "              warm_start=False)\n",
      "['Optimized Gradient Boosting Classifier']\n",
      "[ 0.58064747  0.55548926  0.57106105  0.54778498  0.55003075]\n",
      "Cross-validated logloss 0.561002702355\n",
      "---------------------------------------\n",
      "[ 0.62537118  0.56627308  0.61344228  0.57352499  0.56654021]\n",
      "Cross-validated logloss 0.589030349815\n",
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
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OHZtCbmYCYSGeUUKb6yEyFjLy2j/BHqpnAYOQL5PCYSC7zXKWZ11btwNPGKuA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1634cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featureslist=['Diameter','Spiculation','Eccentricity','MeanHU']\n",
    "def fit_model(X, y, clf):\n",
    "    cv_sets = ShuffleSplit(X.shape[0], n_iter = 5, test_size = 0.20, random_state = 42)\n",
    "    params = {'loss':['deviance','exponential'],'learning_rate':np.arange(0.01,0.13,0.3),\n",
    "             'n_estimators':np.arange(25,200,25),'max_depth':np.arange(2,8,1),\n",
    "             'max_leaf_nodes':np.arange(2,10,1)}\n",
    "    grid = GridSearchCV(clf, params, cv=cv_sets, scoring=\"neg_log_loss\", n_jobs=-1)\n",
    "    grid = grid.fit(X, y)\n",
    "    return grid.best_estimator_\n",
    "\n",
    "optimal_gb=fit_model(inputfeatures[featurelist],malignantlabel,GradientBoostingClassifier())\n",
    "\n",
    "print(optimal_gb)\n",
    "\n",
    "name=[\"Optimized Gradient Boosting Classifier\"]\n",
    "print(name)\n",
    "scores=cross_val_score(optimal_gb,inputfeatures[featurelist], malignantlabel, cv=5, scoring='neg_log_loss')\n",
    "#print(classification_report(Ytest,model.predict(Xtest[featurelist])))\n",
    "print(-scores)\n",
    "print(\"Cross-validated logloss\",-np.mean(scores))\n",
    "print(\"---------------------------------------\")\n",
    "clf=optimal_gb\n",
    "clf.fit(Xtrain[featurelist],Ytrain)\n",
    "roc=roc_curve(Ytest,clf.predict_proba(Xtest[featurelist])[:,1])\n",
    "#print(clf.feature_importances)\n",
    "#ROC curve\n",
    "plt.plot(roc[0],roc[1], alpha=0.5)\n",
    "#plt.plot(rocrandom[0],rocrandom[1])\n",
    "\n",
    "scores=cross_val_score(GradientBoostingClassifier(),inputfeatures[featurelist], malignantlabel, cv=5, scoring='neg_log_loss')\n",
    "#print(classification_report(Ytest,model.predict(Xtest[featurelist])))\n",
    "print(-scores)\n",
    "print(\"Cross-validated logloss\",-np.mean(scores))\n",
    "print(\"---------------------------------------\")\n",
    "clf=GradientBoostingClassifier()\n",
    "clf.fit(Xtrain[featurelist],Ytrain)\n",
    "roc=roc_curve(Ytest,clf.predict_proba(Xtest[featurelist])[:,1])\n",
    "\n",
    "#ROC curve\n",
    "plt.plot(roc[0],roc[1], alpha=0.5)\n",
    "#plt.plot(rocrandom[0],rocrandom[1])\n",
    "plt.title('ROC')\n",
    "plt.ylabel('TPrate')\n",
    "plt.xlabel('FPrate')\n",
    "plt.legend(['Optimized Gradient Boosting', 'Default Gradient Boosting'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)\n"
     ]
    }
   ],
   "source": [
    "featureslist=['Diameter','Spiculation','Eccentricity','MeanHU']\n",
    "def fit_model(X, y, clf):\n",
    "    cv_sets = ShuffleSplit(X.shape[0], n_iter = 5, test_size = 0.20, random_state = 42)\n",
    "    params = {'alpha':np.arange(0.01,0.2,0.01)}\n",
    "    grid = GridSearchCV(clf, params, cv=cv_sets, scoring=\"neg_log_loss\", n_jobs=-1)\n",
    "    grid = grid.fit(X, y)\n",
    "    return grid.best_estimator_\n",
    "\n",
    "optimal_mnb=fit_model(roundedfeatures[featurelist],malignantlabel,MultinomialNB())\n",
    "\n",
    "print(optimal_mnb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes\n",
      "Average precision score: 0.414522511619\n",
      "Area under curve: 0.638017121599\n",
      "Cross-validated logloss 0.585022669107\n",
      "---------------------------------------\n",
      "Multinomial Naive Bayes\n",
      "Average precision score: 0.410012824874\n",
      "Area under curve: 0.645654609834\n",
      "Cross-validated logloss 0.55280005726\n",
      "---------------------------------------\n",
      "Logistic Regression\n",
      "Average precision score: 0.415806776411\n",
      "Area under curve: 0.644832145578\n",
      "Cross-validated logloss 0.55254268468\n",
      "---------------------------------------\n",
      "Random Forest\n",
      "Average precision score: 0.376921978648\n",
      "Area under curve: 0.614972435122\n",
      "Cross-validated logloss 0.563315068589\n",
      "---------------------------------------\n",
      "Gradient Boosting\n",
      "Average precision score: 0.377418451084\n",
      "Area under curve: 0.617288557214\n",
      "Cross-validated logloss 0.557275128346\n",
      "---------------------------------------\n",
      "SVM with rbf kernel\n",
      "Average precision score: 0.271426829941\n",
      "Area under curve: 0.501749137197\n",
      "Cross-validated logloss 0.579370953538\n",
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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usAbCqE+/pbaax+CWp0JBUS5HSkiLJ2bNK8QGBRDv6gKlp3CzNCbndHOMeR2v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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111cd550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_splits=24\n",
    "kfold=StratifiedKFold(n_splits=n_splits, shuffle=False)\n",
    "\n",
    "featurelist=[\"Diameter\",\"MeanHU\",\"Spiculation\",\"Eccentricity\"]\n",
    "models=[GaussianNB(), optimal_mnb,\n",
    "    optimal_lr,\n",
    "        optimal_rf,\n",
    "        optimal_gb,\n",
    "    SVC(C=0.02,kernel='rbf', probability=True),\n",
    "        SVC(C=0.02, kernel='linear', probability=True)]\n",
    "name=[\"Gaussian Naive Bayes\", \"Multinomial Naive Bayes\", \"Logistic Regression\", \"Random Forest\", \"Gradient Boosting\", \"SVM with rbf kernel\", \"SVM with linear kernel\"]\n",
    "\n",
    "predictedmodels={}\n",
    "\n",
    "\n",
    "\n",
    "for nm, clf in zip(name[:-1], models[:-1]):\n",
    "    print(nm)\n",
    "    predicted=[]\n",
    "    mallabelcv=[]\n",
    "    for train,test in kfold.split(inputfeatures,malignantlabel):\n",
    "        if nm==name[1]:\n",
    "            clf.fit(roundedfeatures[featurelist].iloc[train],[malignantlabel[i] for i in train])\n",
    "            predicted.append(clf.predict_proba(roundedfeatures[featurelist].iloc[test])[:,1])\n",
    "        else:\n",
    "            clf.fit(inputfeatures[featurelist].iloc[train],[malignantlabel[i] for i in train])\n",
    "            predicted.append(clf.predict_proba(inputfeatures[featurelist].iloc[test])[:,1])\n",
    "        mallabelcv.append([malignantlabel[i] for i in test])\n",
    "    if nm==name[1]:        \n",
    "        scores=cross_val_score(clf,roundedfeatures[featurelist], malignantlabel, cv=StratifiedKFold(n_splits=n_splits, shuffle=True), n_jobs=-1, scoring='neg_log_loss')\n",
    "    else:\n",
    "        scores=cross_val_score(clf,inputfeatures[featurelist], malignantlabel, cv=StratifiedKFold(n_splits=n_splits, shuffle=True), n_jobs=-1, scoring='neg_log_loss')\n",
    "    predicted=np.concatenate(np.array(predicted),axis=0)\n",
    "    mallabelcv=np.concatenate(np.array(mallabelcv),axis=0)\n",
    "    predictedmodels[nm]=predicted\n",
    "    roc=roc_curve(mallabelcv,predicted)\n",
    "    print(\"Average precision score:\", average_precision_score(mallabelcv,predicted))\n",
    "    print(\"Area under curve:\", auc(roc[0],roc[1]))\n",
    "    plt.plot(roc[0],roc[1])\n",
    "    #print(-scores)\n",
    "    print(\"Cross-validated logloss\",-np.mean(scores))\n",
    "    print(\"---------------------------------------\")\n",
    "    #plt.plot(rocrandom[0],rocrandom[1])\n",
    "plt.title('ROC')\n",
    "plt.ylabel('TPrate')\n",
    "plt.xlabel('FPrate')\n",
    "plt.legend(name)\n",
    "plt.savefig(\"clfroccomparison.png\",dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models averaged: ['Multinomial Naive Bayes', 'Logistic Regression']\n",
      "Area under curve: 0.645943704899\n",
      "Average precision score: 0.413326782175\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119a0f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression logloss 0.551825461897\n",
      "Ensemble of models logloss 0.551930318918\n"
     ]
    }
   ],
   "source": [
    "#Ensemble of multiple models by averaging their prediction outputs\n",
    "\n",
    "predictedmodels=pd.DataFrame(predictedmodels)\n",
    "models=[name[i] for i in [1,2]]\n",
    "predictedmean=np.mean(predictedmodels[models],axis=1)\n",
    "roc=roc_curve(mallabelcv,predictedmean)\n",
    "print(\"Models averaged:\",models)\n",
    "print(\"Area under curve:\", auc(roc[0],roc[1]))\n",
    "print(\"Average precision score:\", average_precision_score(mallabelcv,predictedmean))\n",
    "plt.plot(roc[0],roc[1])\n",
    "plt.title('ROC')\n",
    "plt.ylabel('TPrate')\n",
    "plt.xlabel('FPrate')\n",
    "plt.show()\n",
    "print(\"Logistic Regression logloss\",log_loss(mallabelcv,predictedmodels[name[2]]))\n",
    "print(\"Ensemble of models logloss\",log_loss(mallabelcv,predictedmean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes\n",
      "Average precision score: 0.292869667393\n",
      "Area under curve: 0.505311216148\n",
      "Cross-validated logloss 0.603750022224\n",
      "---------------------------------------\n",
      "Multinomial Naive Bayes\n",
      "Average precision score: 0.289291994353\n",
      "Area under curve: 0.5050129433\n",
      "Cross-validated logloss 0.592039297402\n",
      "---------------------------------------\n",
      "Logistic Regression\n",
      "Average precision score: 0.265452572085\n",
      "Area under curve: 0.482313726429\n",
      "Cross-validated logloss 0.593946852487\n",
      "---------------------------------------\n",
      "Random Forest\n",
      "Average precision score: 0.272393993431\n",
      "Area under curve: 0.468075008001\n",
      "Cross-validated logloss 0.603813293483\n",
      "---------------------------------------\n",
      "Gradient Boosting\n",
      "Average precision score: 0.286135769747\n",
      "Area under curve: 0.50192788764\n",
      "Cross-validated logloss 0.596427710433\n",
      "---------------------------------------\n",
      "SVM with rbf kernel\n",
      "Average precision score: 0.278733456464\n",
      "Area under curve: 0.510783543177\n",
      "Cross-validated logloss 0.59313741474\n",
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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MFXULkrXiiDY/TZPI72hwchwNTo6jnfYeWo30xLmp5XsrCCB6Cm8NHR0dLl26RHp6Otra\n2vz999/K3cUvS54o9OrVC1DYYuc/36Ak8PPzw8/P77Wft7CwYMaMGWzYsOGln8nblPY2WLNmDR4e\nHsTHx1O1alX8/f3R0Hi/uuOCskF2VhYp8XEcXrmMW+fPKBJVVEjTMWW5aSeGhiu+To/ZbcRY/zIj\n5sYCoAGYfvEFxgMCSinyV0P0FN4irVu3ZteuXQCsW7eOnj2f2j9NmTJFaQgHhb9RT5gwgePHj+Pq\n6sr8+fML2GJPmTKFgIAAmjRpgq2tbYEzFObNm4ejoyOOjo4sWLAAUAhMjRo18Pf3p3r16vTu3ZsD\nBw7g7e2NnZ0dZ84oPtT5ewE7d+7E09MTNzc3mjVrRnR0dLFtbtu2LZcvXy70QJwhQ4bg4eGBg4MD\n3333nTI9z1J76dKljB07VpmeP5bVq1dTt25dXF1d+fzzz4u01cgjJSUFHR0dVFVVi6z70KFDBXY+\n//3333Ts2BFQmPZ5eXnh7u5O165dSUlJUf5NnrUwF3xchAX9w875s/ixT0d+HzlQKQgm/f7HBvPB\n7DTqSnNTQ8rJiolj3epZzDgSDoCkrU21o0cwGfw5krp6qbXhVSh7PYU9E+C/f99umRWd4JMXD7EA\n9OjRg2nTptG2bVtCQ0MJCAh4Jc/+WbNmMXfuXKUFxJEjRwrcv3btGocPHyY5ORl7e3uGDBlCaGgo\nK1asICgoCFmW8fT0pHHjxhgaGhIeHs6mTZtYvnw5derUYe3atZw4cYIdO3Ywc+bMAmcRADRo0IDT\np08jSRK//fYbs2fP5v/+7/9eGLOKigrjxo1j5syZrFy5ssC9GTNmYGRkRE5ODr6+voSGhir9kAA6\nd+6Ml5cXc+bMARTOqN988w1Xr15lw4YNnDx5EnV1dYYOHcqaNWsK9Snq3bs3mpqahIWFsWDBAqUo\nFFZ306ZNla6qpqamrFixgoCAAGJjY5k+fToHDhxQ2pPMmzePYcOGFWphLvh4SEtMYMc8xWFPqkYV\neJSrSUwlF3I0LcndGU8/+cky0vDHAJyusp1uQQf575wBALY7d6BeoXTsKl6XsicKpYizszMRERGs\nW7eO1q1bv/Xy27Rpg6amJpqampiZmREdHc2JEyfo2LGj0g+oU6dOHD9+HD8/P2xsbHBycgIUTq6+\nvr5IkoSTk1Oh4/6RkZF0796dqKgoHj9+jI2NzUvF1atXL2bMmMHt27cLpG/cuJFly5aRnZ1NVFQU\nV65cKSAKpqam2Nracvr0aezs7Lh27Rre3t4sXryY8+fPU6dOHQDS09MxMzMrtO684aOYmBjq169P\nq1atsLKyKrLuTz/9lNWrV9O/f39OnTrFqlWr2Lt3L1euXMHb2xuAx48f4+XlVaSFuaDsk5aUyK4f\nZ3P30kUA1PQceajSDFSgxkMVVJ+YG6tV1kbH9S7rb24jSzWTAY+DqBukQjxg2LMHGhYWpdeI16Ts\nicJLvNGXJH5+fnz11VccOXKEuLg4ZXpRNtmvwqvYXz+bX0VFRXmtoqJS6LMjRoxgzJgx+Pn5ceTI\nEaZMmfJScampqfHll18WOH/h9u3bzJ07l7Nnz2JoaIi/v3+hbe7RowcbN26kRo0adOzYEUmSkGWZ\nfv368f33379U/aAQGHd3d4KCgsjNzS2y7v79+9OuXTu0tLTo2rUrampqyLJM8+bNWbdu3XPlFmZh\nLijb5GTnEvjlSNKT4lBR0wDJGlXVJmRIMo5GMibpN8nKUaGO6QEsdMIJeJDEHSMtJsfG0bXLRq6t\nGgpkYfb116XcktdDzCm8ZQICAvjuu++Ub+h5WFtbExwcDEBwcPBzb9UAenp6JCcnP5f+Iho2bMi2\nbdtIS0sjNTWVrVu30rBhw9eKPb/19rNDQcXh7+/PgQMHiImJARTHaero6KCvr090dHSR5z537NiR\n7du3s27dOnr06AEoDgTavHkzDx8qjiKMj48v1s4iLS2NCxcuULVq1RfWXblyZSpXrsz06dOV5zXU\nq1ePkydPEh6uGAdOTU3lxo0bpKSkkJiYSOvWrZk/fz4XL158pd+J4MNk+/8tJz1J8UKnrjMQDd22\n7NZ5zPcV+6Or78+fVeeys/psJlvcI8BQk+vaOnioG+N5rQ03un+F/DgLFV1dVD7QBQ9lr6dQylhY\nWBS6jDTP+tnBwQFPT0+qV3/e58TZ2RlVVVVcXFzw9/cv9swBAHd3d+WZAqBY7unm5lbsstDCmDJl\nCl27dsXQ0BAfH59ChasoNDQ0GDlyJKNGjQLAxcUFNzc3atSogaWlpXJo5lkMDQ2pWbMmV65cUbah\nVq1aTJ8+nRYtWpCbm4u6ujqLFy/Gysrqued79+6NtrY2mZmZ+Pv7U7t2bYAX1t27d29iYmKUzqam\npqYEBgbSs2dPMp8cgDJ9+nT09PQKtTAXlD1ysrORc3P5+9fl3A5WzOkZ95jMvFOx5JDCaeOv2a6W\nyzQTYwA8KtQGFBPLje6l0O/7S6SiWJhh0LULRh/wOR3COlvw0TF8+HDc3NwYMGDAWylPfP4+XNKS\nEgk/e5q/ly0skK5VpQtzVE3xU/mHnzQWsUlPRykIk70mFzgr+WoNxd9ey9kZ8zmz0Sjk5eV9QFhn\nCwSFULt2bXR0dIpdVSUo+2ydPe3pfgNAq5oX2fdVSTKwYpGqLp+r7qSa0Q7665hxTluxyuhZQXi0\ncaPiWWdnbDa+/F6d9xkhCoKPimfPtRZ8fDxOT+PQiqcb0Mr7dOfqPYmqsZVR04I7lhqcShtIxZwo\n+uuYcV3XAA8TR1rbti4gCGnBF/hvsmIPjHkZGloUoiAQCMo8siwTFXadhP8esGfx0y/w8FodiLhu\nSts0xaSwurUuvzruYPOlJHbrmHFdzwh741qsaLVC+Ux2fDyRQ4aS/mThgU59LzQsXs294H1GiIJA\nICizRIVdJ+Hhf+z+aU6B9IcaJmyt4EcbVVPapmWhoqdO5+EumFmVZ9PvXzydUDauRWvbp3uOMsPC\nuOXXHp7MxVb+YRb67du/uwa9A4QoCASCMknW40zWTvqyQNoN565cSVanhoklo25mQUoWlarq0/TT\nGhiaqLNp30imqSmWhT87fwBwp38AyDLatWtjuXQJqnp676w97wohCgKBoMxx9eRRZe+ghndj6nft\nhUHFykz+5hi1knJQS8gCwNbNlOQm1xgTsggeRXDuscLEbrKJVwFByElJ4YZXfchSPGf1xyoklbK5\nzatstqoUyLO/fhMePHhAly5diryfkJDAzz///NL5n8Xf3x8bGxtcXV1xcXHh4MGDbxTv22bp0qWs\nWrWqtMMQfMAkPozm/7q3VQpCJTt7Wg0djZ5JRZaOO0HF+BzUcsHTz5a+M+vzyedO7Lm9m+vxVyEx\nEo/0DCbXGkjXNk9t2LOiorjhUQeyslApVw6b7dvLrCCA2Kfw1tDV1VU6a5YUERERtG3blkuXLr3W\n8/7+/rRt25YuXbpw+PBhBg0aRFhY2BvHlZ2djZrax9vpfB8+fx87qQmPOLZmBVeOKWxIjC2q0G7M\n1xibW7LmRAR3/7xN+TSZKNVcslrd5qH6P8pnr8dfxz5XYkX4JfAaDi1nAIrJ6Zh584n79VcA1K2q\nUHXPng9WEF52n8KH2boPhIiICHx8fHB2dsbX15e7d+8CcPPmTerVq4eTkxOTJk0qcMiOo6MjAJcv\nX1ZaRzs7OxMWFsaECRO4efMmrq6ujB07tkD+nJwcvvrqKxwdHXF2dmbhwoWFB/UELy8v7t+/r7w+\nf/48jRs3pnbt2rRs2ZKoqCgAzp49i7Ozs7LOvPoCAwPx8/PDx8cHX19fAObMmUOdOnVwdnZW2lWn\npqbSpk0bXFxccHR0VJ67UJgldX578ZCQEOrVq4ezszMdO3bk0aNHgMJ2e/z48dStW5fq1au/kgut\noGzx6L8H7FzwA4sDerD080+VguDZsTv95i5mX6TMuCnHeLDmpkIQjFXJ9LvJ7vifOBf99MXSXq8K\nraOf7N73/Y6cpCQyw8K407uPUhD0O3ak2r59H6wgvApl7vXuhzM/cC3+2lsts4ZRDcbXHf/Kz40Y\nMYJ+/frRr18/li9fzsiRI9m2bRujRo1i1KhR9OzZk6VLlxb67NKlSxk1ahS9e/fm8ePH5OTkMGvW\nLC5dukRISAhAASuLZcuWERERQUhICGpqasTHx78wtr179yrPFsjKymLEiBFs374dU1NTpYX18uXL\n6d+/P7/++iteXl5MmDChQBnBwcGEhoZiZGTE/v37CQsL48yZM8iyjJ+fH8eOHSMmJobKlSsrz5lI\nTEwkLi6uWEvqvn37snDhQho3bszkyZOZOnWq8qyI7Oxszpw5w+7du5k6dSoHDhx4uT+IoEyxfNQg\nALR09dDS0aVRnwDsPOujpaNLRkoWV3bcxiouhxwVCb3W5pjZhzLt1CIg3yTyv5thy5Od7TXbkfDX\nbqImFDSyq34mCNXy5d9p20qTMicK7xOnTp3izz//BODTTz9l3LhxyvS8swx69epV6OEtXl5ezJgx\ng8jISDp16oSdnd0L6zpw4ACDBw9WDuMYGRkVmm/s2LFMnDiRyMhITp06BcD169e5dOkSzZs3BxS9\njkqVKpGQkEBycjJeXl7KWPPOegBo3ry5sp79+/ezf/9+pV9TSkoKYWFhNGzYkC+//JLx48fTtm1b\nGjZsSHZ29gstqRMTE0lISKBx48YA9OvXj65dn076derUCVDsTn4djyfBh0/07ZtIkgqynMuw3593\ntz35ZzgV43K4b6rK1EkNUNdUpf9ehYOyUhAep8GeJy97zaaQZdWVqCf/BvTb+6HbtClaNWp8VIIA\nZVAUXueN/n2kV69eeHp6smvXLlq3bs0vv/yCra3tG5c7Z84cunTpwsKFCwkICOD8+fPIsoyDg4NS\nJPIo7lCZvDMcQDH++vXXX/P5558/ly84OJjdu3czadIkfH19mTx58htZUudZgL+MfbigbCHLMnsW\n/R9XTxwBoH7X3s/lifg3luvBD7mnmkOEhTbqmqpsurGJc9Hn8KjgQZvUaiTu/AuO/gCxqWBYg8TA\nK6SdaweA+fx5lP/kk3fZrPeKsj9AVorUr1+f9evXA4rDYPIsrevVq8eWLVsAlPef5datW9ja2jJy\n5Ejat29PaGjoC621mzdvzi+//KL8kixu+Gj48OHk5uayb98+7O3tiYmJUYpCVlYWly9fxsDAAD09\nPYKCgl4YK0DLli1Zvny5crL9/v37PHz4kAcPHlCuXDn69OnD2LFjCQ4OLtaSWl9fH0NDQ+V8wR9/\n/KHsNQg+XmRZJnDMEKUgtB7xFXU7PO1Brg26y1dTj7FrcShyRg6HtbNo72rOphubmHZqGgBtLFtw\np2cvHowdy4O/Ynlw2pAHe5JIPXECOSMD0y/HfNSCACXcU5AkqRXwI6AK/CbL8qxn7usDq4EqT2KZ\nK8vyiucK+gBIS0vDIt8pS2PGjGHhwoX079+fOXPmKI9/BFiwYAF9+vRhxowZtGrVCn19/efK27hx\nI3/88Qfq6upUrFiRiRMnYmRkhLe3N46OjnzyyScMGzZMmX/gwIHcuHEDZ2dn1NXV+eyzz5TnHReG\nJElMmjSJ2bNn07JlSzZv3szIkSNJTEwkOzubL774AgcHB37//Xc+++wzVFRUaNy4caGxArRo0YKr\nV68qh5p0dXVZvXo14eHhjB07FhUVFdTV1VmyZAnJycnFWlKvXLmSwYMHk5aWhq2trfJ3J/g4eZye\nxsqxw0mKUZyx8dmi5ZQ3NWPN6TuE7LuD+cNscnNkbJ7YWV+00+CLBtVQNwxi2j9TAZhc6zO85qwl\nCdCzTMfUKQl8v4Vairk19UqVUNHSKpX2vU+U2JJUSZJUgRtAcyASOAv0lGX5Sr48EwF9WZbHS5Jk\nClwHKsqy/Lioct/XJamvQlpaGtra2kiSxPr161m3bh3bt28v7bAKJSUlRbk6atasWURFRfHjjz+W\nclTvFx/a5+9D5PLRg+z9eT46hkb0nDYbfbOKrDkZwT/rb1ArS4248iqka0pUNdWlXTs7jmXtY/et\n3TT+OQjPG89/x1VtG42GvRt89n7t1SlJ3gfr7LpAuCzLt54EtB5oD1zJl0cG9CRJkgBdIB4o84PE\n58+fZ/i0wxLFAAAgAElEQVTw4ciyjIGBAcuXLy/tkIpk165dfP/992RnZ2NlZUVgYGBphyT4iJBl\nmeNrAzm7QzHc2mOqQhDWBt1VCoJ2bWO+HeiMJElsurGJCdcXcS76HKo5MuOeCIKJQzJIMpK5Kwbf\n/IaakRGoqJdm095bSlIUzIF7+a4jAc9n8iwCdgAPAD2guyzLuc/kQZKkQcAggCpVqpRIsO+Shg0b\nfjBHO3bv3p3u3buXdhiCMk5KfBxpSYkA5GZnc2b7ZlRUVbkdcp7H6WmAYneyQYWKAOw/fY86WWqo\nV9Eh4DMXgAJzBx4VPGhj0QKYhqlTEiYOKdB7M9g1f/eN+8Ao7dVHLYEQwAeoCvwtSdJxWZaT8meS\nZXkZsAwUw0fvPEqBQFBixD+4z4rRz69aAzCsVBkNLS0+nb2QcuUV81mrj91GPzwNUKVZa8WKvPyC\nMNlrMi3DdYjqO5FcQM6VYPRl0LcotA5BQUpSFO4DlvmuLZ6k5ac/MEtWTGyES5J0G6gBnEEgEHwU\n/DX/ewDsvRpi790IADV1Dao4uqCazz5lbdBdtofcx+ByMs5Zami5G3FO+wj/27tbuUN5stdk2mvU\n4eYYxQoiTf0sDCcuEYLwCpSkKJwF7CRJskEhBj2AXs/kuQv4AsclSaoA2AO3SjAmgUDwnnD7wjku\n7N1JzN0IAFoNG4OaeuHj/GuD7jJx679YZ6nQ8LEmGlX1GDDIlf57f+R6/HU8KnjQurwd3uOmcvNa\nDgAV3BIxGjQcard7V00qE5SYKMiynC1J0nBgH4olqctlWb4sSdLgJ/eXAv8DAiVJ+heQgPGyLMeW\nVEwCgeD9ICU+jj9nTVFe9529sFBByOsdBN+Kp2mGOh6P1TCqrEPnEW5PN6Rlq7DiwkH+O3mQR+GK\nDZWVOtpi0L03uD77HioojhKdU5BleTew+5m0pfl+fgC0KMkY3hWqqqo4OTmRnZ2NjY0Nf/zxBwYG\nBm9c7ps6oxbFlClT+PXXXzE1NQWgVatWzJo1q5inXo+QkBAePHhA69ati88sKPPk5uSwbvJYABp/\nOgAnnxZoltN5Ll9e70AzFwZllqNcpoxDI3O8OtiyPWIL04KmA9A6IYbc1DQehVcCwHrzZrQdHd5d\ng8oYpT3RXGbQ1tZWGtX169ePxYsX880335RyVC9m9OjRhfouFUdOTg6qqqovnT8kJIRz584JUfjI\nyc3NYe/i+codySqqqjg09i1UEAC2h9xHL1diqEp5yMrik2HOWDuZsGXXaObf/Rs9YOqFZBw023L9\nb8Xud9MxY4QgvCHC5qIEyG9LnZKSgq+vL+7u7jg5OSk3qUVERFCzZk0+++wzHBwcaNGiBenp6YBi\nH4OLiwsuLi4sXrxYWW5GRgb9+/fHyckJNzc3Dh8+DChsrDt06EDz5s2xtrZm0aJFzJs3Dzc3N+rV\nq1es5UV+Dh48iJubG05OTgQEBJCZmQmAtbU148ePx93dnU2bNnHz5k1atWpF7dq1adiwIdeuKZxp\nN23ahKOjIy4uLjRq1IjHjx8zefJkNmzYgKurq9I6W/DxsX3OdKUgeHbsxoCffkVbr3CzubVBd7l0\nM55+GdrIaRlc9drP1PtjGbG+F3bj9vL7jzn8/mMOFsfKkfhEEMq3aYNBp47vqjllljLXU/hv5kwy\nr75d62zNmjWoOHHiS+XNycnh4MGDDBigsOPV0tJi69atlC9fntjYWOrVq4efnx8AYWFhrFu3jl9/\n/ZVu3bqxZcsW+vTpQ//+/Vm0aBGNGjVi7NixyrIXL16MJEn8+++/XLt2jRYtWnDjxg0ALl26xIUL\nF8jIyKBatWr88MMPXLhwgdGjR7Nq1Sq++OKL52KdP38+q1evBuCHH36gcePG+Pv7c/DgQapXr07f\nvn1ZsmSJ8lljY2OCg4MB8PX1ZenSpdjZ2REUFMTQoUM5dOgQ06ZNY9++fZibm5OQkICGhgbTpk3j\n3LlzLFq06DX/AoIPndycHG4FnwVg0M+B6BmbAE/nDPJ4pHqMRNUzZCWp0yOzC+o5qmypuYiHuXfw\nyLTH9cgD1HMgvrYBNVsrbFx0GzRAw8rq3TeqjFLmRKG0SE9Px9XVlfv371OzZk2lDbUsy0ycOJFj\nx46hoqLC/fv3iY6OBlAejQlPbaATEhJISEigUSPF0rxPP/2UPXv2AHDixAlGjBgBQI0aNbCyslKK\nQtOmTdHT00NPTw99fX3atVOsuHByciI0NLTQmJ8dPrp48SI2NjZUr14deDoMlicKeZvYUlJS+Oef\nfwrYWef1KLy9vfH396dbt25Ki2uBICdHYVTg7NuKneFpbN+sMF8Muq3oxXraGPFI9RhR6qvRytKh\nU8SX6GXpEe59kCokMTo0AdfgcyTf0wagzucj0WjUs3QaU8Ypc6Lwsm/0b5u8OYW0tDRatmzJ4sWL\nGTlyJGvWrCEmJobz58+jrq6OtbU1GRkZwFMLaFBMVOcNH70O+ctSUVFRXquoqLw1e+k8q+zc3FwM\nDAyUcyj5Wbp0KUFBQezatYvatWtz/vz5t1K34MPiwY1rRFw8z/V/jqOmqakwtAFuZ2oxc+u/gEII\nPG2MaO9qrjCuO7UazaxyDLwzDTlbg7adHmMha5O65Sp3j5iQ5w9cYUgPNBp0K52GfQSUOVEobcqV\nK8dPP/1Ehw4dGDp0KImJiZiZmaGurs7hw4e5c+fOC583MDDAwMCAEydO0KBBA9asWaO817BhQ9as\nWYOPjw83btzg7t272NvbK4d03hR7e3siIiIIDw+nWrVqRVpWly9fHhsbGzZt2kTXrl2RZZnQ0FBc\nXFy4efMmnp6eeHp6smfPHu7du/dCy29B2SHx4X9snzOd9OQkUh49nceqXL0mWrq66JmYsu2xYtho\nZkcn1A2D2H1rN38/gnPXzqGVpcPAe9PIjVejTfnvMD8eQlKkFlFnFAc5VZj8LQbt26OiU/jEtODt\nIEShBHBzc8PZ2Zl169bRu3dv2rVrh5OTEx4eHtSoUaPY51esWEFAQACSJNGixdMVu0OHDmXIkCE4\nOTmhpqZGYGBggR7Cm6KlpcWKFSvo2rUr2dnZ1KlTh8GDBxead82aNQwZMoTp06eTlZVFjx49cHFx\nYezYsYSFhSHLMr6+vri4uFClShVmzZqFq6srX3/9tfBSKoPERd5l1biR5OZkY1jZguo1HKhRvyGV\nqtmja2QMKOYPjmz9F08boyc9gyc+RVqVaBPjR5WIhuRmwycGM6iiGUJGghr3TyoEQUVHB6NeYs/B\nu6DErLNLirJgnS0oW3ysn787oSFEhAZzbuefBdJHrd6q3IiWfyI5b/6ga9NI9v6nWHQw9lZNMh72\nI0vWporGebzLB2KkFoncZyuRczaScvQolb7/nvItW6BSrtw7bF3Z432wzhYIBGUQOTeX5V98TkJ0\nlDKtkp09ddp1pqqHJyr59rBsD7nPlagkalXUo2UVmR5msay7uxFd2ZCB4U1JTmpMhQpZOLe0xc7d\nAzm9L4knzvCg7dMDpPSaNxeC8A4RoiAQCF6JWxfOKQWhz/cLqGBbrdB8a4PuEnQ7Hk9rQzakDyI2\nVpUNoXNx4RtcgAxkPNwSqBPgh4q6GsmHDhE56gvIygJA086OSrO+R1VXzCG8S4QoCASCl0aWZXb9\nOBuAXtP/74WCMHHrv6iTzeqYLmwqB5flwRiq5BCuv5n65l7Ur+aAaQUjUg4eIGHjJlL/+QcAs/Hj\n0alfHy376u+sXYKnCFEQCATFkvU4k/tXLxO8ZwdZmYol1ZXs7J/LlzeHEHQ7DjuDHVjoH+ar5Lro\nRH+CeZId5veP0eLQMeAYaUD+tXiqJiYYdO2CcX//d9EkQREIURAIBMVy9I/lXNy/S3ndb87zu9OV\nvQODIDxtt5OUbodN2HcYp1XmsVYqhvaRVDu2BRUdHcwXLCjwrKqBPtpOTiXeDkHxCFEQCASFkvIo\nnmOrl3Pn3xDSEhMA6Pm/uZQ3MVUuM4V89taP9mBkdYascveRo71oc6sbanqZNPjEGOPLZ0j4ZSUA\nlr+uopy7W6m0SVA8whDvLREdHU2vXr2wtbWldu3aeHl5sXXr1jcqc8qUKcydOxeAyZMnc+DAgdcq\nJyQkhN27dxd678iRI+jr6+Pq6oqzszPNmjXj4cOHrx3zs0RERLB27Vrl9blz5xg5cuRbK19Qcqye\nMIqrJ46QlphADe/GtB7+JZWr1yggCKBYYRQZ9YDaRtvR0rxHmzsNaHyrB9bVtfEzvIzq+B4krFII\ngtlXXwpBeM8RPYW3gCzLdOjQgX79+im/AO/cucOOHTuey5udnY2a2qv/2qdNm/ba8RVnXd2wYUP+\n+usvAL7++msWL17M1KlTX7u+/OSJQq8nG488PDzw8Ch2qbSglMhISSF4zw5ObVZ8jlVU1Ri+Yj3q\nmlrKPHk9g0eqx0hWOY2OHINtpXhuqko0vdOCytHtqOpqjLdHDpF9VgBQaeZM9Fo0R1VXt1TaJXh5\nRE/hLXDo0CE0NDQK7P61srJSmtcFBgbi5+eHj48Pvr6+RdppA8yYMYPq1avToEEDrl+/rkz39/dn\n8+bNgMJau3HjxtSuXZuWLVsSFaVYHtikSRPGjx9P3bp1qV69OsePH38l62pZlklOTsbQ0BCA+Ph4\nOnTogLOzM/Xq1VMa6xWVfvToUVxdXXF1dcXNzY3k5GQmTJjA8ePHcXV1Zf78+Rw5coS2bdsCip5Q\nQEAATZo0wdbWlp9++kkZy//+9z/s7e1p0KABPXv2VPaYBCWHLMscXL5EKQjGFlXoM34M6vvGwV+j\nCfv9M/bP7kXuzi+wTJxElPpqUlTDsSQatdwsfO92VAiCtUyVBT2J7NMbAItFCzHo1FEIwgdCmesp\nHN94g9h7KW+1TBNLXRp2K3p53OXLl3F3d39hGcHBwYSGhmJkZER2dnahdtrBwcGsX7+ekJAQsrOz\ncXd3p3bt2gXKycrKYsSIEWzfvh1TU1M2bNjAN998w/LlywFFT+TMmTPs3r2bqVOncuDAgWKtq/O+\ntOPi4tDR0WHmzJkAfPfdd7i5ubFt2zYOHTpE3759CQkJKTJ97ty5LF68GG9vb1JSUtDS0mLWrFnM\nnTtX2RM5cuRIgbqvXbvG4cOHSU5Oxt7eniFDhhASEsKWLVu4ePEiWVlZhf4eBG+R1FhI/o+9qzdw\n7ewFAEa0M0BDKxO2Ktx206RyGOSq4QbsMdJkn7HCrXRy7CP86n7H4X9rczM6AYuo41Q5sgEJGQ0r\nK0yGD0evWbPSapngNShzovA+MGzYME6cOIGGhgZnzyo85Js3b46RkcLHpSg77ePHj9OxY0fKPdm9\nmXfuQn6uX7/OpUuXlNbcOTk5VKpUSXk/z646z4r7Zcg/fPTDDz8wbtw4li5dyokTJ9iyZQsAPj4+\nxMXFkZSUVGS6t7c3Y8aMoXfv3nTq1AkLC4ti627Tpg2amppoampiZmZGdHQ0J0+epH379mhpaaGl\npaW0ARe8ZXKyYF1PCP+blCwNroR7AtDH+gIa4YoXq+RylpxIrsiQrNF42hhRxTpUaVEx2WsyTbRa\nsXHZvyTHPqLaza1Y3juIBFhv3IC2s3NptUzwBpQ5UXjRG31J4eDgoPySBMVhOLGxsQXGznXyOTu+\nyE67OGRZxsHBgVOnThV6P88gT1VV9bUss/38/OjcufMrPwcwYcIE2rRpw+7du/H29mbfvn3FPvOs\nffjbsvkWFENSFMxTmDM+zNDhj9uKnq5VVQsq9BkEGjqsja3KxG2XAYVfUay8mb3/KXzHvq03mVrR\n9dmy4Twa0mPcgn/EIOkWpqNGYjx4MJIklU67BG+MmFN4C/j4+JCRkcGSJUuUaWlpaUXmL8pOu1Gj\nRmzbto309HSSk5PZuXPnc8/a29sTExOjFIWsrCwuX778wvhexbr6xIkTVK1aFXhq1Q2KYR8TExPK\nly9fZPrNmzdxcnJi/Pjx1KlTh2vXrr2Wbba3tzc7d+4kIyODlJQUZS9G8BY5s0zxf0197jdUHPnq\n2LQFHacthFp+jL0Zzv+CR6Jd5Rcc3New979FnIs+h0cFDybVnozhyVocWXMd8+oGeN36BYOkW1Q7\ndhSTIUOEIHzglLmeQmkgSRLbtm1j9OjRzJ49G1NTU3R0dPjhhx8KzV+Unba7uzvdu3fHxcUFMzMz\n6tSp89yzGhoabN68mZEjR5KYmEh2djZffPEFDg5FH1betGnTF1pX580pyLKMvr4+v/32G/B0ItjZ\n2Zly5cqxcuXKF6YvWLCAw4cPo6KigoODA5988gkqKiqoqqri4uKCv78/bm7FL0esU6cOfn5+ODs7\nU6FCBZycnNDX1y/2OcErEKE41/hGk9XcO3UCgIa9+rHhfBSB/64jSn01ajpQRdsJs/KamJX34BOr\n1jjGe3N+wx3CYqPx9LOldisrIo5ABqBuZlZ67RG8NYR1tuC9JCUlBV1dXdLS0mjUqBHLli0rdjK/\ntPggPn+yDPG34PKfPD4wk9BHlTj60FZ5+7Ea7GyqwaPH2ajp3AagVcXhzGn5OY8zsrl8/AEXD94j\nNSETE0tdGnSxw9zekJzkZG7UqYtu48ZY/rK0tFoneAmEdbbgg2bQoEFcuXKFjIwM+vXr994KwntP\nWjycmAf/LARgk54OoQmOGDxU9LzSNLPZV/chCZijoypRXlsdE20n/F070rlqZ87uus3Fg/fITMvG\n3N4An741sKxppBwiSjl2DAAVPb3SaZ/grSNEQfBekn8XtOA1SI2DzETY8ClEX2KTng5Hcy3JfqCN\nXbRiv8BqL4kMDQ0sNboz1KknvTyrKB/PTM9m1+JQ7l6Jx8bFhNqtrKlgU75AFbIskxOnODjHdPgw\nBGUDIQoCQVkjNQ7+rzrkZrNJT4e9JhXJjTTD8baid5CqpsZpA2+qazegvat5ATEASIxJZ9fPoSRG\np9G0Tw1qNahc4H5uWhpp585xb9DnyjTpLR4LKyhdhCgIBB84m25sYvet3ZCbDQ+vKBLNjECvIuey\nE2h+xgzzWMVms0PGjblcvhYzOzo9JwapiZk8CEvg2PobyLky7Ua5YmGv2N0uyzIZ//5L9KwfSA8O\nLvCc5bJfUM+3V0bwYSNEQSD4gNl0YxPTTil8sTzSFXtd1DJVMbtriXaaLpVyDCifLJGqZ0aYY2d0\ntQ2Y+UzvIDdX5t/DkZzefpPsx7kYVChHm6HOGFR4egRm9P/+x6O165TXuj4+GAf0R7t2bbEEtYwh\nREEg+EDJLwiTY+PompzKTTV3tv2bt1Eyl2jtKqgaqtO7T2dq1G/0XBnxD1I59MdVom8nUcXBGLcW\nVahgXR51TdUC+TIuK3ogFj//jG7jRkiqqs+VJSgbiM1rb4kZM2bg4OCAs7Mzrq6uBAUFMXXqVL7+\n+usC+UJCQpTLF62trWnYsGGB+66urjg6Or5WDPXr1weet6sODAxk+PDhr1xekyZNeHb5L0BmZibN\nmjUr1GCvqGdKioiIiNf+fX3o7L6lsEPPE4SgeBulIOh6tmJZlf7srNiGmv2+fE4QkuMzOLbuOhtm\nnCHxYTrN+tei7XBnLOwNlYKQuPMv4gIDue5Zj/SLF9Hx9kbPp6kQhDJOifYUJElqBfwIqAK/ybI8\nq5A8TYAFgDoQK8ty45KMqSQ4deoUf/31F8HBwWhqahIbG8vjx4/p2bMnrVq14vvvv1fmXb9+PT17\n9lReJycnc+/ePSwtLbl69eobxfHPkzNun7Wrfh1ycnKKvHfhgsI0LSQk5LXLL6pOVfGF81JsurJG\nscM4PYOuyankVq7DiasKe+sGPfsxL9qCTNX45+YOEqLTOL/vDjdO/weAvVdF6rWviurDu8Qu3KTM\nl3zoEJnXrj2tUF0do4D+76ZxglKlxHoKkiSpAouBT4BaQE9Jkmo9k8cA+Bnwk2XZAehaUvGUJFFR\nUZiYmCh9fExMTKhcuTLVq1fH0NCQoKAgZd6NGzcWEIVu3bop37bXrVtX4F5+hg0bpjyfoWPHjgQE\nBACwfPlyvvnmGwB0n1gTP2tXDfDgwQNatWqFnZ0d48aNK7QOa2trxo8fj7u7O5s2Kb4g/vjjD2Xv\n5cyZMzx8+JA+ffpw9uxZXF1duXnzZqFl5ebm4u/vz6RJkwDYv38/Xl5euLu707VrV1JSUgqtszD7\nb1AIxtixY6lTpw7Ozs788ssvRf9ByirpCWwK/Z3+2zoz7azi/ap1lgpRjX5iQ6RiH4dDk2bcrORJ\n0O14PG2MlIIQG5nCvt8usXbKacLORuPQ2Jw+073w+bQmqtF3uNW2HbE//6z8L08QbHZsp/q5s9T8\nNxRdb+/SabfgnVKSPYW6QLgsy7cAJElaD7QHruTL0wv4U5bluwCyLL/xkV+HA5fx8M6tNy2mAGZW\ntjT1H1Tk/RYtWjBt2jSqV69Os2bN6N69O40bKzo8PXv2ZP369Xh6enL69GmMjIyws7NTPtu5c2f6\n9+/PV199xc6dO1mzZg1//PHHc3U0bNiQ48eP4+fnx/3795VnKBw/fpwePXoUyPusXXVgYCAhISFc\nuHABTU1N7O3tGTFiBJaWls/VY2xsTPCT1SVLly4lLS2NkJAQjh07RkBAAJcuXeK3334rUP6zZGdn\n07t3bxwdHfnmm2+IjY1l+vTpHDhwQGn/MW/ePCZPnlxonYXZf//+++/o6+tz9uxZMjMz8fb2pkWL\nFh/PJGfkOfjNl90VzbiuoYHH48e0Tk2l84BzzP9c8dlU0dFnXXpVjm79FwA/x0pEXn/ExYP3iAiN\nRV1TFdfmVXBtVoVy5TUAxRDRg7FjATAaEECFJz8LPl5Kck7BHLiX7zrySVp+qgOGkiQdkSTpvCRJ\nfQsrSJKkQZIknZMk6VxMTEwJhfv66Orqcv78eZYtW4apqSndu3cnMDAQgO7du7N582Zyc3OfGzoC\nxReioaEh69evp2bNmkrb7GfJE4UrV65Qq1YtKlSoQFRUFKdOnVLOJbwIX19f9PX10dLSolatWkoT\nvmd51hcpL95GjRqRlJREQkJCsXV9/vnnSkEAOH36NFeuXMHb2xtXV1dWrlxZoP5n6yzM/nv//v2s\nWrUKV1dXPD09iYuLIywsrNhYPnjOB8K6XvCbr+JarxL2epb87rsUg/ITWTlVMdFsam3Lac/hBCdp\n4GljxMyOThiEJLJ9/gWiwhOo09aGvjPrU79TNaUg5KamKgVB290dsy++KI0WCt4zSnv1kRpQG/AF\ntIFTkiSdlmX5Rv5MsiwvA5aBwvvoRQW+6I2+JFFVVaVJkyY0adIEJycnVq5cib+/P5aWltjY2HD0\n6FG2bNlSqOV19+7dGTZsmFJICsPc3JyEhAT27t1Lo0aNiI+PZ+PGjejq6qL3EhYDL2tRnd/iG3ju\nTfxl3szr16/P4cOH+fLLL9HS0kKWZZo3b866desKzf9snYXZf8uyzMKFC2nZsmWBvC97ZsQHSVo8\n7Byl+LmCI2gZgKEp5R5mce0/FUIP7EXXyJhKdva0GjqGXTvvUqtSeZa0c+bI2mtEhCdiUKEcXb/2\nQEPr6T91WZZJ/HMrUU9Eu3zbtpjPnVMaLRS8h5SkKNwH8o9PWDxJy08kECfLciqQKknSMcAFuMEH\nxPXr11FRUVEOC4WEhGBlZaW837NnT0aPHo2trW2hB8907NiRqKgoWrZsyYMHD4qsp169eixYsIBD\nhw4RFxdHly5d6NKly3P5Xseuuig2bNhA06ZNOXHiBPr6+i/lVjpgwACOHTtGt27d+PPPP6lXrx7D\nhg0jPDycatWqkZqayv3796le/eXPvmjZsiVLlizBx8cHdXV1bty4gbn5sx3PMsbeCWzS02F3BRs0\n1WypGJSC44NYAPbwfwCo1e/I9rQKbN95l7D7STRV02bT92dR11LFu0s1HBuZo6ahmLxPDTpDVuQ9\nor6dDLm5qFeujJaTE5W/n1lqTRS8f5SkKJwF7CRJskEhBj1QzCHkZzuwSJIkNUAD8ATml2BMJUJK\nSgojRowgISEBNTU1qlWrxrJly5T3u3btysiRI1m4cGGhz+vp6TF+/Phi62nYsCH79++nWrVqWFlZ\nER8f/9ySVgBnZ+cCdtV5Zy6/DlpaWri5uZGVlaU88vNlGDNmDImJiXz66aesWbOGwMBAevbsSWZm\nJgDTp09/JVEYOHAgERERuLu7I8sypqambNu27ZXb88EQuglCN7C7ohnhubl02PpIeSvW2ICYKm3J\nUdPg8CU1JOLxK1+eevHqaGRnU6mWEb79aqKj/7R3GLN4MbELnx7HWq5OHaos/x1JXf2dNkvw/lOi\n1tmSJLVGsdxUFVguy/IMSZIGA8iyvPRJnrFAfyAXxbLVBS8qU1hnC9433trnLzsTTi2Cg9OUSf2r\nu6KfUBnLo8kkGVixWr8FOSpqeNoojnY1TMrBJQ5yEx5Tqao+nu1tqWxnoBzmy01LI2HrVhLWrycz\nLBzL339Dw8oaDYsy3ssSPMd7YZ0ty/JuYPczaUufuZ4DiAFNwcdHVCj8ktfTk4CnL2i5Fd1YkeNK\nzqXzWN5UDAUeNGqAh7UZ7aqZUT1dhQv775D9OBddEy3qD3LE1s30uTmfmIWLiF+xAgBdX1+xrFRQ\nLMWKgiRJ5YAvgSqyLH8mSZIdYC/LsjgjUSB4HZIewMmfIOjJ8a3VmkFlxT6DpAw4H2VA1O1bJIRd\nxwXFHE6mlj4WVawYamDMv5vucBYwqFAO12aW1KhXCVX15xcSpgUHE79iBRq2tths2SycTAUvxcv0\nFFYA5wGvJ9f3gU3AeyUKsix/PGvWBe8NrzX8en2PQhA0y5Nj5sQZrS7EX4oE4NrJo4pyJUjTzuH+\nJyb80v0P/H/8//buOz6qKu/j+OdMekIS0gkpEFoo0gMIUkRWpIjILiq6NsRVKS52n91H0VXXxWd3\n7QULRVwBF0VBpClFkJqAdAiEdAgQ0nsyM+f54w5jgBACZBKS/N6vV17OrXOOTOabe+8pm+l5pIx9\nJTAeYN4AACAASURBVOl0vTGcLoNb4h/qddHPfOEvm+3NTYOfexaTh8eVVVA0OTUJhbZa67uUUncD\naK2L1TX27evu7k5WVhYBAQESDKLOaK3JysrC3d295gfFzoYdnwJwqM/7rPj0U+BLAJz9vcn3rKDQ\nw0z2rRFgUgSq63n8H5vokVoOJsXoKd1o3S3wkm+T9vDDAARMfgzvG2+83KqJJqwmoVCulPLAdsNT\nKdUWKHNoqS5TeHg46enpXIsd20Tj5u7uXmUz4wtoDav/F7Z9YCxHj2L/lu0oF2cK/SDtRh+2FRpj\nSo1oMY3i5G44mTVFhwvpUlFBnpcTkaMjaxQI5jNGs1XXVq0Inj79iusmmqaahMLLwCogQin1JXAD\nRmuha4aLiwtRUVH1XQwhqlaYCUsehsQNlLq3ZJvbbRzbmk1hbjyFfrCq/2mivfyI8YohUF3PN+vC\n6VKex1CzK64Vzrj38GPyn7pjcqrZAAQ5i4yxtAIeffQSewpxoUuGgtZ6jVJqJ3A9RhOJ6VrrMw4v\nmRAN3ezhkJMChSftqxaeHEh2xgEAWnXrSVLzBKL9o5k7Yi5fbkthwVeHmFTmTHOrieBW3gy5J5rg\nVj4Xe4dzWIuKyJo9hzMffgiA762ja79OotGrSeujtVrrYcAPVawTQlTlh2cgzRgdN7nFHzhV6Mb+\n1HJyTxkDGe4a+CSrPeLIcNmPZ0YHJr21mRYJJYyyuOIU4MaICdG0uu7ynpEdu3UMZttAiUFPTEe5\nutZ+vUSjd9FQUEq5A55AoFLKD+MqAcCHCwe2E0KcdWw9lh2fUW51hj+t57vX/o0l77exIb8OvZ1I\nV0/ynHbgVuHJoJQ7aJ1ZRoWzCc/BITx4d+cah4G2WrHm52MpLLIHQoe4WJxsw6gLcbmqu1J4FHgC\naInRJPXspzQfeP9iBwnRJKXtgF2fU1wGm37azv68gcb6Z/8KwMFmHSmKuR2tTAyM2keW5VM6HWhF\nj7RJuJo96HZTOH3GROHmUfP+pDmLF3PyxRnnrGvxyt8kEMRVuegnUGv9DvCOUupxrXXVg/YIIQA4\n/O5EjuR4c7QgEGgBQLp7S455RtE6wIshgwdy//Be/PfAYtZ8k0K7rFvwLQvCJbKc39/fh8Dwmn2R\nm3NyKIuPJ2fBQgrWrAHAJSIC//vuw6NXLzyu6+KoKoomoiYPmt9TSl2HMXuae6X18x1ZMCGuebmp\nkJNMSVEJP6Qard9KPP3JtrixJHQs/doEMKFHmH32s6LcMpL+Y6Vnzu9wDjFzy/hul/XcoPTQIZLG\nGXNN4OyMc8tQWrzwIt43DXVI9UTTVJMHzS8BN2KEwgqM6TV/ASQURJOirVZS9++lvKwEtCbuw+ex\naoVFm4BmePkp3mt+lzHJTaUwKCkoJ25lMns3puFm9SGh7wbeeuiV6t/sPNbiYk7bplb1GTOG4Gee\nxiUkpLarKESN+imMx5jj4Fet9USlVAjwH8cWS4hrw6nEBFIP7CV26deUFOSft9UYlyjXww/taeJw\nh9t4/Yau9jAA0FbNF++voyLFlcNBO/g17EemD5xco/e2lpaSs3ARWZ99hiUrCwC3Tp1o+X9vSM99\n4TA1CYUSrbVVKWVWSvkApzl38hwhGqUNX8xm5/Jv7cv+7mX4OxfSOyAdV5MFgBfDP2J1ujOvj+vK\na5XCAIxAmP3uKipS3NgduhZzvxNMbzOZOzrcUe37WktLyVu6jJMvvWRf59axI35/vAffMWMkEIRD\n1SQU4pRSzYFPMVohFQIXzikpRGNhtZJ9PMUeCKOGdaBV6nw8nSug90TQMdD7QRamNmf10sP0i/Ln\nzl5hZKYVkH2iiOyMIvYfPUpBRhluxd7siFjO6PH9uTP675d8a221cvLlv5FXaQKh9r9swjnw0sNb\nCFEbqg0F28B3/9Ba5wKzlFKrAB+t9d46KZ0Qda2iFP1aCHMPG/McjAiNp9OJTcZvStc7YIwxB9SC\n7an8dek+TBpucfJk9tObMJdbjXOYNNluWeR4nsSpQymjh/bnzujqrw4AdEUFR28car9V1H7rFpy8\nvVHO9T2VumhKqv20aa21UmoF0NW2nFwXhRKirmityc88hcVs4Xj8AY7HbuDoEWOUeA93F7o89Dp0\ntc2DrRQLtqeydPdx4hKz6V7uxAi3ZpTEZdG6WyAd+oYQV76ZmfGvYDVZmNF/xiVvFVUux+Gu3QAw\n+fgQ9u9/4XwV06gKcaVq8ifILqVUH611rMNLI0QdKi0sZM6Tj1KSn3fO+mbOFsxOHvzS53F+3uoB\nW7cB4FqhOZOYTwuLiakWD9zM4OPjTP9Ho9nptYHXE98i7lQcmLisQADInj3b/rrtqpU4+/vXTiWF\nuEw1CYV+wL1KqWSgCNu8gVrrbo4smBCOdDLhCF/+71P25RFTnmRbUi7pJ5N5uvQV7in/K1vSS+jX\n2p3mhVZCM80E5llRuIKTIrKjHz1vjmSLWsurSR8bYQDEhMQwqs2oywoEgNP/+jcA7Tasl0AQ9aom\noXCLw0shRB07Gwih7aMZ9/xLfHswly/jV/OBy1wwQacQb25uHolbUhE5J0tx93Kh8y3htIsJYX3R\nKpYkz2dJMlcdBvDb7G2eMTG4tGhRa3UU4kpcakC8x4B2wD5gttbaXFcFE6K2lRUX8/Vr/0txvtHf\nwNnVDcZM58kvNvL3U49xj1sBWsNx73H0sPYhdWsmXpHeDHugE+1ignF2cWLxkcW8ut3oeBYTEnNV\nYQBQcfIk6VOmAuAqc4KIa0B1VwqfAxXAJoxezJ0BmcZJNFjr5s7i5LGjuHp40HnQUHqOGMOrq3cx\nK/MBSrQ3O0vGc9h6O7mnvHD1KCFmdGv63hp1Tr+AFYkrgMt/ZlBZcVwc6U8+CRVmLLm59vXBTz9V\nzVFC1I3qQqGz1rorgFJqNrCjbookRO1J3b+HLYsXcPzwAfu6lhOmEL7rZVr85xXesASwpuAJjpUN\nxKqdaNHGh2F/CKNt72BcXJ2qPGdMSMwVB0J5ejpnPv0US+YZfH//e0zu7rhERuB3552YPD2v6JxC\n1KbqQqHi7AuttVl6UYqGJHnPLo7t3MHu1cvt63qPHktbvxIitt0OQKm1GV9kvY9Vu9F1SARdBrUk\nIKzq0UoXH1nMisQVxGfHE+0ffUVl0lqTfMedWHJyjMHsXpqByc3tis4lhKNUFwo9lFJnB3tRgIdt\n+Wzro5rNEShEHSsrLuab1415Blw9PIm5dRz9x9/Nws1HuG7FHWwtvYcEcz+Kra2xWq2MntKN1t0u\n7DF8NgjgwgfKl8NSWIg5I4Osz2ZjyckBoP26dVdTRSEcprpQ2KO17llnJRHiKmirldPJicRv3UTs\nsm8ACOvYmQl/+z8AFnwdh15/kK8sbwFWnII8ua5rEK27BxIefW4nsbNhUDkIruSBsrW8nNI9e0i5\n7/5z1rdetPAqaiqEY1UXCrrOSiHEVagoK+WDSXdjqbDf8aTjDUO4+ZFpsP1jUjZ+T86xp4BwBvjN\np+MTM/EIubDpZ1VhcKUti0rj40kae7t92aVlS4Kfew6v6/vh1Lz55VdSiDpSXSgEK6Uu2hxCa/2m\nA8ojxGXb8+NKeyCMe/4lglpHsXHDVubN/ITALCdOVTyOooI2gfvo+cI74O57wTkWH1nMK1t/a2p6\nNc1Mi7bvIPWBBwDwGjCAwMmP4dGrF8qp6gfXQlxLqgsFJ6AZv83NLMQ1IefkCVL27qa8pJjDv2wg\nL/M0AP3GDObHZduw5idjLfYDupJhO8b3jlaMGFZ1P8zKgXA1TU211iSOHEV5cjIAHr16ETlndvUH\nCXGNqS4UMrTWlzc9lBB14OvXXiQ/8xQA3s6lBHt4YPFuwcHNnTFrT3ycTtKx2VdY2oYTPWISPhEt\ncXI2XXCe828XXU4gmDMzyVu2jPK0NHIXfYVyd0eXltq3t1mxAtfWrWqhtkLUrepCQa4QxDVj7ZyP\n2L36h3PW3R2VTGr5AHYWTwBA+WSQEODEm48NQzndAl4BFz3f1dwuOmeuZBvPvn1wi4oCFIFTJuPk\ne+EtKiEagupCYdjVnlwpNQJ4B+NW1Gda65kX2a8PxsQ9E7TWX1/t+4rGpbSwkN2rf8DTN4CWwaEU\nnzxBuXN/vs01+gvkeJs43MqVPWcC6ezrg/Kpfu7iK71dpLXGfPIk2Z8b05P73Xcfwc8+gzKZZM4D\n0Whc9JOstc6+mhMrpZyAD4CbgXQgVim1TGt9sIr93gDWXM37icanrLiYHz/9mPgtawEoLQki/cxw\nnJ3LCG3XHEugF28dTKfApOnn4kbnUB/G9gizH1+5n0FlV3K7yFpWRtLv/0D5sWP2dYFTJmNydb2a\nKgpxzXHknzd9gQStdSKAUmoRMBY4eN5+jwPfAH0cWBbRgFjMVo7HZ7Ps389RVmQ8RDa7hdAjspjr\nmcw27z7M8Z7K9sNpYILXx3XlnvPmRwYu2gP5cm4XaauV4u3bSZ34EAAurSIJfPQx3Nq3k0lwRKPk\nyFAIA9IqLadjzM1gp5QKA8YBQ6kmFJRSjwCPAERGXvjLLxqX5e/vIfXAUcptgVDQqj2TfNYSYU4B\n4AvfhwHoF+XP2B5hFwTC+UNSzB0xt0bvezYArCUlAFhLSjjx9DP27c4hIbT5/nu5OhCNWn3fCH0b\neF5rba1ubCWt9SfAJwAxMTHSqa4RK84rJfHXeVhLjds0o1oeppPnJjAD/m1h0NP8p+eN1Z6jciDU\nZEgKXVFBwdp1FP78M3nffnvhDiYTreZ/jkfv3sgYYKKxc2QoHAciKi2H29ZVFgMssv2iBQKjlFJm\nrfV3DiyXuEbN++og2WtWoW2B0CcgjY4+mXD7LIjsB/5tqj3+Sq8QUic9TPEOYxBgn9vG4G/reAZg\ncnfHtU0bCQPRZDgyFGKB9kqpKIwwmADcU3kHrbV9VhGl1DxguQRCE5OXTu6mJaz52YO8zBx0yUYA\nJrWNpXnf8dB3HrS89BBcVTUxvRStNaUHD9oDoc3KFbi2bi0BIJo0h4WCbbjtacBqjCapc7TWB5RS\nj9m2z3LUe4uG4eDmNHYvWMjJnMNoy2n7+iHXR9L8z9+B06U/nlfaAc1aXk58r95gNiYTDJw61dbP\nQIimzaHPFLTWK4AV562rMgy01g86siziGmOpYM1HL2Gp+C0MvG+4jUlTJuLk7HLJw6908DpzVhZ5\n33/P6Zlv2Ne127Be5kYWwqa+HzSLpig/g4rPRmKpCMfLxRO3KF9WhT/EV4/2r9HhV9IbWWttNC19\ncKJ9ndegQYS99SZOzaqeWEeIpkhCQdQprTWJH7zAqkODgCQsfh34W0X/c9sqV6GqCW9qcqvIkpdH\nxgsvUBwbZ58P2WfUKIKeehLX8PCrrY4QjY6EgqgzlsxjfP/WdpLTw7GU7QTgE9fuAOf0RD7f+VcG\nl9P5rCwhgYIffzI6mwUFEThtGt7DbpJhKYS4CPnNEA6XdbyQvf9dS+IRTUFxoj0Qvgu5lTwXz4v2\nSIarG9baWlZG6iSjo1vIX/6C14ABV1kTIRo/CQXhUOWlZha9ugMT7kS6bCC3dB8A37b5I2GtWzO5\nih7JZ13tPAfJ48fbh7N279r1KmohRNMhoSAcKnFdLAA9PL5kywljjMU8vyjCWreu9sHy5QRC0fYd\nFG/fds46rTVlRxMA6LhvL8rl0i2ahBASCsKBVs7aR+LuErQuPycQvg0ZTedqjqsuEAo2bKBo8xb7\nsjZXkLtwkbFwXqczk5cX4e+9K4EgxGWQUBC1Ij+rBHO5FXO5hd0/pkBZIYn7SrCaT1BesMi+X3yP\nu+ms1EUfLFcXCJaCAjLfepuyhARMXl7GSqsV5epK0PTpBEx6yHEVFKKJkFAQV+3wtgzWzjt0zjqt\nLQSZNuNsXUsyvhT4hvPih+9dtGNadT2TreXl5C9bRsYLLwLgffPNhL/3rgNrJETTJaEgrpjWmq3f\nHuPXNakAxIxujb/5IC7b32LfmUKO5AYBvuQ5e9Nx0l8vCISq+h5Ubm5aevAgRVu3cfqf/7Qf4z1i\nBCHPP1c3FRSiCZJQEJdNWzUpB7JYN/8QJQUVBEY0Y8jd0YR4pKI+/iNHywM4UmA8NVgaMpqHR/et\ncs6D8/sejG75O4ZnBKGPVpAwdTgVab9Nx+HRsyct/vYy7h061F1FhWiCJBTEZakos/DjnAMk7Tlj\nX+fcB47+9wFalK4nqdCPZceNQFjY8g6emjC02kCY0X8Gw3aUkz13LhXHX7tgbPWWb8zEo1cvXCMi\nEEI4noSCqDFt1Xz/7m5OJuZxw/h2tGplptmX1+Oypcy+z9LCAUA+Ke2H89SYCwMBsN8ymtF/BuPb\njOPwbV0xNWuG79ixKFdX/B98AJQJ11aRKCenuqqeEAIJBVFDuaeLWf7+HvJOlzBgdDA9tvWFX4xp\nKzO1L0ntHqL7bVOxPGIMOPfua3+u8jyLjyxm9/FYfl/UgSHL00lPmA6Ac1AQLd+YWTeVEUJclISC\nuKSMY3ks+acxNEXPYS3pHDsUTCVk+V7Ha5mDUBZfwpZvZtNyIxBCO3QEQJvNmE+fJmfhIiyFBQCY\n4n9kwS4LcIgsDoGLC27t29Nq4YJ6qZsQ4lwSCqJa2qpZ8i8jECK7BBCdNhE3UzEAA04+Tde8/fTP\n2QzAkPsmYXJypuMNgynZu5fkO+8651xO/v5EleZR4WIicPRtBD//HM5+fnVbISFEtSQUxEVlHS8k\n9ock0NDrllaktICA1Qf5Nbcl35UN44Gir3Ery8fD24fbn3uRlh06Yc7JIe2hhyndvx8wehWHvPgC\n3sOG4eTtzcRVxtXE3BH/qM+qCSEuQkJBVOnE0Vy+/fcu+/KxABNfLFvF7c5OrMtoS4D7KfzDwmkZ\nPYReI26jmZs7ibePo+zwYfsxER/PwmvwYJRSRp+EzSuIz44n2j+6PqokhKgBCQVxgcy0AlZ+bIxm\nOmxCBC32v0DCikTeKsvhgxPG8NOtunZn7DMvULJnD+U7Yjn63PMAKBcXfMeNI3DqFFxCQoCqZ0oT\nQlybJBTEOZZ/sIeUfVm4ujvhOSCITbs+p2viATZntgaML3mlTIyc9jSnZr5B9rx55xwfvXcPqtLA\ndFc7/LUQom5JKAi7suIKUvZl4e3vjmlEKOtXzue5soUsz+wEQNuW19EtIohmHp6cevpZCteuBSDs\nvXdx79AB59BQCQQhGjgJBYG5wsLp5Hy+/fevAOgYf75YvoIxaVtZbjUCoXdSBiF7jlEGlFU6NuLj\nWTQbMuSCc0ogCNEwSSg0cSkHslj+3h77sgYK417nnaLN/GD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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa6a8828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_splits=24\n",
    "kfold=StratifiedKFold(n_splits=n_splits, shuffle=False)\n",
    "\n",
    "featurelist=[\"Diameter\",\"MeanHU\",\"Spiculation\",\"Eccentricity\"]\n",
    "models=[GaussianNB(), optimal_mnb,\n",
    "    optimal_lr,\n",
    "        optimal_rf,\n",
    "        optimal_gb,\n",
    "    SVC(C=0.02,kernel='rbf', probability=True),\n",
    "        SVC(C=0.02, kernel='linear', probability=True)]\n",
    "name=[\"Gaussian Naive Bayes\", \"Multinomial Naive Bayes\", \"Logistic Regression\", \"Random Forest\", \"Gradient Boosting\", \"SVM with rbf kernel\", \"SVM with linear kernel\"]\n",
    "\n",
    "predictedmodels={}\n",
    "\n",
    "for nm, clf in zip(name[:-1], models[:-1]):\n",
    "    print(nm)\n",
    "    predicted=[]\n",
    "    mallabelcv=[]\n",
    "    for train,test in kfold.split(inputfeatures,randomlabel):\n",
    "        if nm==name[1]:\n",
    "            clf.fit(roundedfeatures[featurelist].iloc[train],[randomlabel[i] for i in train])\n",
    "            predicted.append(clf.predict_proba(roundedfeatures[featurelist].iloc[test])[:,1])\n",
    "        else:\n",
    "            clf.fit(inputfeatures[featurelist].iloc[train],[randomlabel[i] for i in train])\n",
    "            predicted.append(clf.predict_proba(inputfeatures[featurelist].iloc[test])[:,1])\n",
    "        mallabelcv.append([randomlabel[i] for i in test])\n",
    "    if nm==name[1]:        \n",
    "        scores=cross_val_score(clf,roundedfeatures[featurelist], randomlabel, cv=StratifiedKFold(n_splits=n_splits, shuffle=True), n_jobs=-1, scoring='neg_log_loss')\n",
    "    else:\n",
    "        scores=cross_val_score(clf,inputfeatures[featurelist], randomlabel, cv=StratifiedKFold(n_splits=n_splits, shuffle=True), n_jobs=-1, scoring='neg_log_loss')\n",
    "    predicted=np.concatenate(np.array(predicted),axis=0)\n",
    "    mallabelcv=np.concatenate(np.array(mallabelcv),axis=0)\n",
    "    predictedmodels[nm]=predicted\n",
    "    roc=roc_curve(mallabelcv,predicted)\n",
    "    print(\"Average precision score:\", average_precision_score(mallabelcv,predicted))\n",
    "    print(\"Area under curve:\", auc(roc[0],roc[1]))\n",
    "    plt.plot(roc[0],roc[1])\n",
    "    #print(-scores)\n",
    "    print(\"Cross-validated logloss\",-np.mean(scores))\n",
    "    print(\"---------------------------------------\")\n",
    "    #plt.plot(rocrandom[0],rocrandom[1])\n",
    "plt.title('ROC')\n",
    "plt.ylabel('TPrate')\n",
    "plt.xlabel('FPrate')\n",
    "plt.legend(name)\n",
    "plt.savefig(\"clfroccomparison.png\",dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sensitivity Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes\n",
      "Average precision score: 0.267390179676\n",
      "Area under curve: 0.519349199946\n",
      "Cross-validated logloss 0.581929959138\n",
      "---------------------------------------\n",
      "Multinomial Naive Bayes\n",
      "Average precision score: 0.410012824874\n",
      "Area under curve: 0.645654609834\n",
      "Cross-validated logloss 0.552565700265\n",
      "---------------------------------------\n",
      "Logistic Regression\n",
      "Average precision score: 0.277523278995\n",
      "Area under curve: 0.53277643315\n",
      "Cross-validated logloss 0.552853636955\n",
      "---------------------------------------\n",
      "Random Forest\n",
      "Average precision score: 0.307595764349\n",
      "Area under curve: 0.570125722738\n",
      "Cross-validated logloss 0.566301162721\n",
      "---------------------------------------\n",
      "Gradient Boosting\n",
      "Average precision score: 0.320184953328\n",
      "Area under curve: 0.59362534176\n",
      "Cross-validated logloss 0.558803268982\n",
      "---------------------------------------\n",
      "SVM with rbf kernel\n",
      "Average precision score: 0.263748343031\n",
      "Area under curve: 0.497165075523\n",
      "Cross-validated logloss 0.571000436593\n",
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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D91YHEZaUS9cG7sx4pAm+bsZ+DK2mmH//XsnRtX9hYW1D35deoUm3XmpW8j1C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\nS4pCRES0pChERETLP9ZVzRFdlWH4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118a66a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_splits=24\n",
    "kfold=StratifiedKFold(n_splits=n_splits, shuffle=False)\n",
    "\n",
    "featurelist=[\"Diameter\",\"MeanHU\",\"Spiculation\",\"Eccentricity\"]\n",
    "models=[GaussianNB(), optimal_mnb,\n",
    "    optimal_lr,\n",
    "        optimal_rf,\n",
    "        optimal_gb,\n",
    "    SVC(C=0.02,kernel='rbf', probability=True),\n",
    "        SVC(C=0.02, kernel='linear', probability=True)]\n",
    "name=[\"Gaussian Naive Bayes\", \"Multinomial Naive Bayes\", \"Logistic Regression\", \"Random Forest\", \"Gradient Boosting\", \"SVM with rbf kernel\", \"SVM with linear kernel\"]\n",
    "\n",
    "predictedmodels={}\n",
    "\n",
    "\n",
    "\n",
    "for nm, clf in zip(name[:-1], models[:-1]):\n",
    "    print(nm)\n",
    "    predicted=[]\n",
    "    mallabelcv=[]\n",
    "    for train,test in kfold.split(inputfeatures,malignantlabel):\n",
    "        if nm==name[1]:\n",
    "            clf.fit(roundedfeatures[featurelist].iloc[train],[malignantlabel[i] for i in train])\n",
    "            predicted.append(clf.predict_proba(roundedfeatures[featurelist].iloc[test])[:,1])\n",
    "        else:\n",
    "            clf.fit(roundedfeatures[featurelist].iloc[train],[malignantlabel[i] for i in train])\n",
    "            predicted.append(clf.predict_proba(inputfeatures[featurelist].iloc[test])[:,1])\n",
    "        mallabelcv.append([malignantlabel[i] for i in test])\n",
    "    if nm==name[1]:        \n",
    "        scores=cross_val_score(clf,roundedfeatures[featurelist], malignantlabel, cv=StratifiedKFold(n_splits=n_splits, shuffle=True), n_jobs=-1, scoring='neg_log_loss')\n",
    "    else:\n",
    "        scores=cross_val_score(clf,roundedfeatures[featurelist], malignantlabel, cv=StratifiedKFold(n_splits=n_splits, shuffle=True), n_jobs=-1, scoring='neg_log_loss')\n",
    "    predicted=np.concatenate(np.array(predicted),axis=0)\n",
    "    mallabelcv=np.concatenate(np.array(mallabelcv),axis=0)\n",
    "    predictedmodels[nm]=predicted\n",
    "    roc=roc_curve(mallabelcv,predicted)\n",
    "    print(\"Average precision score:\", average_precision_score(mallabelcv,predicted))\n",
    "    print(\"Area under curve:\", auc(roc[0],roc[1]))\n",
    "    plt.plot(roc[0],roc[1])\n",
    "    #print(-scores)\n",
    "    print(\"Cross-validated logloss\",-np.mean(scores))\n",
    "    print(\"---------------------------------------\")\n",
    "    #plt.plot(rocrandom[0],rocrandom[1])\n",
    "plt.title('ROC')\n",
    "plt.ylabel('TPrate')\n",
    "plt.xlabel('FPrate')\n",
    "plt.legend(name)\n",
    "plt.savefig(\"clfroccomparison.png\",dpi=300)\n",
    "plt.show()"
   ]
  }
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